format-version: 1.2 data-version: releases/2024-03-31 subsetdef: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension "" subsetdef: http://purl.obolibrary.org/obo/valid_for_go_gp2term "" subsetdef: http://purl.obolibrary.org/obo/valid_for_go_ontology "" subsetdef: http://purl.obolibrary.org/obo/valid_for_gocam "" subsetdef: ro-eco "" subsetdef: RO:0002259 "" ontology: aio property_value: http://purl.org/dc/terms/description "None" xsd:string property_value: http://purl.org/dc/terms/license https://creativecommons.org/licenses/unspecified property_value: http://purl.org/dc/terms/title "Artificial Intelligence Ontology" xsd:string property_value: owl:versionInfo "2024-03-31" xsd:string [Term] id: APOLLO_SV:00000008 name: software development def: "A planned process that has specified output a software product and that involves the creation of source code." [] is_a: OBI:0000011 ! planned process property_value: IAO:0000117 "Mathias Brochhausen" xsd:string property_value: IAO:0000117 "William R. Hogan" xsd:string property_value: IAO:0000119 "http://en.wikipedia.org/wiki/Software_development" xsd:string property_value: IAO:0000600 "A planned process resulting in a software product involving the creation of source code." xsd:string [Term] id: APOLLO_SV:00000796 name: dataset creating def: "A planned process that has a data set as its specified output." [] is_a: OBI:0000011 ! planned process intersection_of: OBI:0000011 ! planned process intersection_of: OBI:0000299 IAO:0000100 ! has_specified_output data set relationship: OBI:0000299 IAO:0000100 ! has_specified_output data set property_value: IAO:0000111 "creating a data set" xsd:string property_value: IAO:0000117 "William R. Hogan" xsd:string property_value: IAO:0000118 "data set creation" xsd:string property_value: IAO:0000118 "dataset creation" xsd:string [Term] id: ARTIFICIAL-INTELLIGENCE-ONTOLOGY:0000000 name: root node [Term] id: BFO:0000001 name: entity disjoint_from: ObsoleteClass ! Obsolete Class property_value: BFO:0000179 "entity" xsd:string property_value: BFO:0000180 "Entity" xsd:string property_value: IAO:0000112 "Julius Caesar" xsd:string property_value: IAO:0000112 "the Second World War" xsd:string property_value: IAO:0000112 "Verdi’s Requiem" xsd:string property_value: IAO:0000112 "your body mass index" xsd:string property_value: IAO:0000116 "BFO 2 Reference: In all areas of empirical inquiry we encounter general terms of two sorts. First are general terms which refer to universals or types:animaltuberculosissurgical procedurediseaseSecond, are general terms used to refer to groups of entities which instantiate a given universal but do not correspond to the extension of any subuniversal of that universal because there is nothing intrinsic to the entities in question by virtue of which they – and only they – are counted as belonging to the given group. Examples are: animal purchased by the Emperortuberculosis diagnosed on a Wednesdaysurgical procedure performed on a patient from Stockholmperson identified as candidate for clinical trial #2056-555person who is signatory of Form 656-PPVpainting by Leonardo da VinciSuch terms, which represent what are called ‘specializations’ in [81" xsd:string property_value: IAO:0000116 "Entity doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example Werner Ceusters 'portions of reality' include 4 sorts, entities (as BFO construes them), universals, configurations, and relations. It is an open question as to whether entities as construed in BFO will at some point also include these other portions of reality. See, for example, 'How to track absolutely everything' at http://www.referent-tracking.com/_RTU/papers/CeustersICbookRevised.pdf" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/0000004", type="owl:Axiom", comment="per discussion with Barry Smith", seeAlso="http://www.referent-tracking.com/_RTU/papers/CeustersICbookRevised.pdf"} property_value: IAO:0000600 "An entity is anything that exists or has existed or will exist. (axiom label in BFO2 Reference: [001-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/001-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000002 name: continuant def: "An entity that exists in full at any time in which it exists at all, persists through time while maintaining its identity and has no temporal parts." [] is_a: BFO:0000001 ! entity disjoint_from: BFO:0000003 ! occurrent relationship: BFO:0000050 BFO:0000002 {all_only="true"} ! part of continuant property_value: BFO:0000179 "continuant" xsd:string property_value: BFO:0000180 "Continuant" xsd:string property_value: IAO:0000111 "continuant" xsd:string property_value: IAO:0000116 "BFO 2 Reference: Continuant entities are entities which can be sliced to yield parts only along the spatial dimension, yielding for example the parts of your table which we call its legs, its top, its nails. ‘My desk stretches from the window to the door. It has spatial parts, and can be sliced (in space) in two. With respect to time, however, a thing is a continuant.’ [60, p. 240" xsd:string property_value: IAO:0000116 "Continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. For example, in an expansion involving bringing in some of Ceuster's other portions of reality, questions are raised as to whether universals are continuants" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/0000007", type="owl:Axiom"} property_value: IAO:0000412 http://purl.obolibrary.org/obo/pato.owl property_value: IAO:0000600 "A continuant is an entity that persists, endures, or continues to exist through time while maintaining its identity. (axiom label in BFO2 Reference: [008-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/008-002", type="owl:Axiom"} property_value: IAO:0000601 "if b is a continuant and if, for some t, c has_continuant_part b at t, then c is a continuant. (axiom label in BFO2 Reference: [126-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/126-001", type="owl:Axiom"} property_value: IAO:0000601 "if b is a continuant and if, for some t, cis continuant_part of b at t, then c is a continuant. (axiom label in BFO2 Reference: [009-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/009-002", type="owl:Axiom"} property_value: IAO:0000601 "if b is a material entity, then there is some temporal interval (referred to below as a one-dimensional temporal region) during which b exists. (axiom label in BFO2 Reference: [011-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/011-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x y) (if (and (Continuant x) (exists (t) (continuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [009-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/009-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x y) (if (and (Continuant x) (exists (t) (hasContinuantPartOfAt y x t))) (Continuant y))) // axiom label in BFO2 CLIF: [126-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/126-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Continuant x) (Entity x))) // axiom label in BFO2 CLIF: [008-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/008-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Material Entity x) (exists (t) (and (TemporalRegion t) (existsAt x t))))) // axiom label in BFO2 CLIF: [011-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/011-002", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000003 name: occurrent def: "An entity that has temporal parts and that happens, unfolds or develops through time." [] is_a: BFO:0000001 ! entity relationship: BFO:0000050 BFO:0000003 {all_only="true"} ! part of occurrent property_value: BFO:0000179 "occurrent" xsd:string property_value: BFO:0000180 "Occurrent" xsd:string property_value: IAO:0000116 "BFO 2 Reference: every occurrent that is not a temporal or spatiotemporal region is s-dependent on some independent continuant that is not a spatial region" xsd:string property_value: IAO:0000116 "BFO 2 Reference: s-dependence obtains between every process and its participants in the sense that, as a matter of necessity, this process could not have existed unless these or those participants existed also. A process may have a succession of participants at different phases of its unfolding. Thus there may be different players on the field at different times during the course of a football game; but the process which is the entire game s-depends_on all of these players nonetheless. Some temporal parts of this process will s-depend_on on only some of the players." xsd:string property_value: IAO:0000116 "Occurrent doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. An example would be the sum of a process and the process boundary of another process." xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/0000006", type="owl:Axiom", comment="per discussion with Barry Smith"} property_value: IAO:0000116 "Simons uses different terminology for relations of occurrents to regions: Denote the spatio-temporal location of a given occurrent e by 'spn[e]' and call this region its span. We may say an occurrent is at its span, in any larger region, and covers any smaller region. Now suppose we have fixed a frame of reference so that we can speak not merely of spatio-temporal but also of spatial regions (places) and temporal regions (times). The spread of an occurrent, (relative to a frame of reference) is the space it exactly occupies, and its spell is likewise the time it exactly occupies. We write 'spr[e]' and `spl[e]' respectively for the spread and spell of e, omitting mention of the frame." xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/0000012", type="owl:Axiom"} property_value: IAO:0000600 "An occurrent is an entity that unfolds itself in time or it is the instantaneous boundary of such an entity (for example a beginning or an ending) or it is a temporal or spatiotemporal region which such an entity occupies_temporal_region or occupies_spatiotemporal_region. (axiom label in BFO2 Reference: [077-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/077-002", type="owl:Axiom"} property_value: IAO:0000601 "b is an occurrent entity iff b is an entity that has temporal parts. (axiom label in BFO2 Reference: [079-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/079-001", type="owl:Axiom"} property_value: IAO:0000601 "Every occurrent occupies_spatiotemporal_region some spatiotemporal region. (axiom label in BFO2 Reference: [108-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/108-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Occurrent x) (exists (r) (and (SpatioTemporalRegion r) (occupiesSpatioTemporalRegion x r))))) // axiom label in BFO2 CLIF: [108-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/108-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (iff (Occurrent x) (and (Entity x) (exists (y) (temporalPartOf y x))))) // axiom label in BFO2 CLIF: [079-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/079-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000004 name: independent continuant def: "A continuant that is a bearer of quality and realizable entity entities, in which other entities inhere and which itself cannot inhere in anything." [] def: "b is an independent continuant = Def. b is a continuant which is such that there is no c and no t such that b s-depends_on c at t. (axiom label in BFO2 Reference: [017-002])" [] {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/017-002", type="owl:Axiom"} is_a: BFO:0000002 ! continuant disjoint_from: BFO:0000020 {type="owl:AllDisjointClasses"} ! specifically dependent continuant disjoint_from: BFO:0000020 ! specifically dependent continuant disjoint_from: BFO:0000031 ! generically dependent continuant relationship: BFO:0000050 BFO:0000004 {all_only="true"} ! part of independent continuant property_value: BFO:0000179 "ic" xsd:string property_value: BFO:0000180 "IndependentContinuant" xsd:string property_value: IAO:0000112 "a chair" xsd:string property_value: IAO:0000112 "a heart" xsd:string property_value: IAO:0000112 "a leg" xsd:string property_value: IAO:0000112 "a molecule" xsd:string property_value: IAO:0000112 "a spatial region" xsd:string property_value: IAO:0000112 "an atom" xsd:string property_value: IAO:0000112 "an orchestra." xsd:string property_value: IAO:0000112 "an organism" xsd:string property_value: IAO:0000112 "the bottom right portion of a human torso" xsd:string property_value: IAO:0000112 "the interior of your mouth" xsd:string property_value: IAO:0000601 "For any independent continuant b and any time t there is some spatial region r such that b is located_in r at t. (axiom label in BFO2 Reference: [134-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/134-001", type="owl:Axiom"} property_value: IAO:0000601 "For every independent continuant b and time t during the region of time spanned by its life, there are entities which s-depends_on b during t. (axiom label in BFO2 Reference: [018-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/018-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x t) (if (and (IndependentContinuant x) (existsAt x t)) (exists (y) (and (Entity y) (specificallyDependsOnAt y x t))))) // axiom label in BFO2 CLIF: [018-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/018-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x t) (if (IndependentContinuant x) (exists (r) (and (SpatialRegion r) (locatedInAt x r t))))) // axiom label in BFO2 CLIF: [134-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/134-001", type="owl:Axiom"} property_value: IAO:0000602 "(iff (IndependentContinuant a) (and (Continuant a) (not (exists (b t) (specificallyDependsOnAt a b t))))) // axiom label in BFO2 CLIF: [017-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/017-002", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000015 name: process def: "An occurrent that has temporal proper parts and for some time t, p s-depends_on some material entity at t." [] def: "p is a process = Def. p is an occurrent that has temporal proper parts and for some time t, p s-depends_on some material entity at t. (axiom label in BFO2 Reference: [083-003])" [] {type="owl:Axiom", IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/083-003"} is_a: BFO:0000003 ! occurrent property_value: BFO:0000179 "process" xsd:string property_value: BFO:0000180 "Process" xsd:string property_value: IAO:0000112 "a process of cell-division, \\ a beating of the heart" xsd:string property_value: IAO:0000112 "a process of meiosis" xsd:string property_value: IAO:0000112 "a process of sleeping" xsd:string property_value: IAO:0000112 "the course of a disease" xsd:string property_value: IAO:0000112 "the flight of a bird" xsd:string property_value: IAO:0000112 "the life of an organism" xsd:string property_value: IAO:0000112 "your process of aging." xsd:string property_value: IAO:0000116 "BFO 2 Reference: The realm of occurrents is less pervasively marked by the presence of natural units than is the case in the realm of independent continuants. Thus there is here no counterpart of ‘object’. In BFO 1.0 ‘process’ served as such a counterpart. In BFO 2.0 ‘process’ is, rather, the occurrent counterpart of ‘material entity’. Those natural – as contrasted with engineered, which here means: deliberately executed – units which do exist in the realm of occurrents are typically either parasitic on the existence of natural units on the continuant side, or they are fiat in nature. Thus we can count lives; we can count football games; we can count chemical reactions performed in experiments or in chemical manufacturing. We cannot count the processes taking place, for instance, in an episode of insect mating behavior.Even where natural units are identifiable, for example cycles in a cyclical process such as the beating of a heart or an organism’s sleep/wake cycle, the processes in question form a sequence with no discontinuities (temporal gaps) of the sort that we find for instance where billiard balls or zebrafish or planets are separated by clear spatial gaps. Lives of organisms are process units, but they too unfold in a continuous series from other, prior processes such as fertilization, and they unfold in turn in continuous series of post-life processes such as post-mortem decay. Clear examples of boundaries of processes are almost always of the fiat sort (midnight, a time of death as declared in an operating theater or on a death certificate, the initiation of a state of war)" xsd:string property_value: IAO:0000602 "(iff (Process a) (and (Occurrent a) (exists (b) (properTemporalPartOf b a)) (exists (c t) (and (MaterialEntity c) (specificallyDependsOnAt a c t))))) // axiom label in BFO2 CLIF: [083-003] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/083-003", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000016 name: disposition is_a: BFO:0000017 ! realizable entity disjoint_from: BFO:0000023 ! role property_value: BFO:0000179 "disposition" xsd:string property_value: BFO:0000180 "Disposition" xsd:string property_value: IAO:0000112 "an atom of element X has the disposition to decay to an atom of element Y" xsd:string property_value: IAO:0000112 "certain people have a predisposition to colon cancer" xsd:string property_value: IAO:0000112 "children are innately disposed to categorize objects in certain ways." xsd:string property_value: IAO:0000112 "the cell wall is disposed to filter chemicals in endocytosis and exocytosis" xsd:string property_value: IAO:0000116 "BFO 2 Reference: Dispositions exist along a strength continuum. Weaker forms of disposition are realized in only a fraction of triggering cases. These forms occur in a significant number of cases of a similar type." xsd:string property_value: IAO:0000600 "b is a disposition means: b is a realizable entity & b’s bearer is some material entity & b is such that if it ceases to exist, then its bearer is physically changed, & b’s realization occurs when and because this bearer is in some special physical circumstances, & this realization occurs in virtue of the bearer’s physical make-up. (axiom label in BFO2 Reference: [062-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/062-002", type="owl:Axiom"} property_value: IAO:0000601 "If b is a realizable entity then for all t at which b exists, b s-depends_on some material entity at t. (axiom label in BFO2 Reference: [063-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/063-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x t) (if (and (RealizableEntity x) (existsAt x t)) (exists (y) (and (MaterialEntity y) (specificallyDepends x y t))))) // axiom label in BFO2 CLIF: [063-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/063-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Disposition x) (and (RealizableEntity x) (exists (y) (and (MaterialEntity y) (bearerOfAt x y t)))))) // axiom label in BFO2 CLIF: [062-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/062-002", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000017 name: realizable entity def: "A specifically dependent continuant that inheres in continuant entities and are not exhibited in full at every time in which it inheres in an entity or group of entities. The exhibition or actualization of a realizable entity is a particular manifestation, functioning or process that occurs under certain circumstances." [] is_a: BFO:0000020 ! specifically dependent continuant disjoint_from: BFO:0000019 ! quality relationship: BFO:0000050 BFO:0000017 {all_only="true"} ! part of realizable entity property_value: BFO:0000179 "realizable" xsd:string property_value: BFO:0000180 "RealizableEntity" xsd:string property_value: IAO:0000112 "the disposition of this piece of metal to conduct electricity." xsd:string property_value: IAO:0000112 "the disposition of your blood to coagulate" xsd:string property_value: IAO:0000112 "the function of your reproductive organs" xsd:string property_value: IAO:0000112 "the role of being a doctor" xsd:string property_value: IAO:0000112 "the role of this boundary to delineate where Utah and Colorado meet" xsd:string property_value: IAO:0000600 "To say that b is a realizable entity is to say that b is a specifically dependent continuant that inheres in some independent continuant which is not a spatial region and is of a type instances of which are realized in processes of a correlated type. (axiom label in BFO2 Reference: [058-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/058-002", type="owl:Axiom"} property_value: IAO:0000601 "All realizable dependent continuants have independent continuants that are not spatial regions as their bearers. (axiom label in BFO2 Reference: [060-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/060-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x t) (if (RealizableEntity x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (bearerOfAt y x t))))) // axiom label in BFO2 CLIF: [060-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/060-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (RealizableEntity x) (and (SpecificallyDependentContinuant x) (exists (y) (and (IndependentContinuant y) (not (SpatialRegion y)) (inheresIn x y)))))) // axiom label in BFO2 CLIF: [058-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/058-002", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000019 name: quality is_a: BFO:0000020 ! specifically dependent continuant relationship: BFO:0000050 BFO:0000019 {all_only="true"} ! part of quality property_value: BFO:0000179 "quality" xsd:string property_value: BFO:0000180 "Quality" xsd:string property_value: IAO:0000112 "the ambient temperature of this portion of air" xsd:string property_value: IAO:0000112 "the color of a tomato" xsd:string property_value: IAO:0000112 "the length of the circumference of your waist" xsd:string property_value: IAO:0000112 "the mass of this piece of gold." xsd:string property_value: IAO:0000112 "the shape of your nose" xsd:string property_value: IAO:0000112 "the shape of your nostril" xsd:string property_value: IAO:0000600 "a quality is a specifically dependent continuant that, in contrast to roles and dispositions, does not require any further process in order to be realized. (axiom label in BFO2 Reference: [055-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/055-001", type="owl:Axiom"} property_value: IAO:0000601 "If an entity is a quality at any time that it exists, then it is a quality at every time that it exists. (axiom label in BFO2 Reference: [105-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/105-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (exists (t) (and (existsAt x t) (Quality x))) (forall (t_1) (if (existsAt x t_1) (Quality x))))) // axiom label in BFO2 CLIF: [105-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/105-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Quality x) (SpecificallyDependentContinuant x))) // axiom label in BFO2 CLIF: [055-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/055-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000020 name: specifically dependent continuant def: "A continuant that inheres in or is borne by other entities. Every instance of A requires some specific instance of B which must always be the same." [] def: "b is a relational specifically dependent continuant = Def. b is a specifically dependent continuant and there are n > 1 independent continuants c1, … cn which are not spatial regions are such that for all 1 i < j n, ci and cj share no common parts, are such that for each 1 i n, b s-depends_on ci at every time t during the course of b’s existence (axiom label in BFO2 Reference: [131-004])" [] {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/131-004", type="owl:Axiom"} def: "b is a specifically dependent continuant = Def. b is a continuant & there is some independent continuant c which is not a spatial region and which is such that b s-depends_on c at every time t during the course of b’s existence. (axiom label in BFO2 Reference: [050-003])" [] {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/050-003", type="owl:Axiom"} is_a: BFO:0000002 ! continuant disjoint_from: BFO:0000031 ! generically dependent continuant relationship: BFO:0000050 BFO:0000020 {all_only="true"} ! part of specifically dependent continuant property_value: BFO:0000179 "sdc" xsd:string property_value: BFO:0000180 "SpecificallyDependentContinuant" xsd:string property_value: IAO:0000111 "specifically dependent continuant" xsd:string property_value: IAO:0000112 "of one-sided specifically dependent continuants: the mass of this tomato" xsd:string property_value: IAO:0000112 "of relational dependent continuants (multiple bearers): John’s love for Mary, the ownership relation between John and this statue, the relation of authority between John and his subordinates." xsd:string property_value: IAO:0000112 "Reciprocal specifically dependent continuants: the function of this key to open this lock and the mutually dependent disposition of this lock: to be opened by this key" xsd:string property_value: IAO:0000112 "the disposition of this fish to decay" xsd:string property_value: IAO:0000112 "the function of this heart: to pump blood" xsd:string property_value: IAO:0000112 "the mutual dependence of proton donors and acceptors in chemical reactions [79" xsd:string property_value: IAO:0000112 "the mutual dependence of the role predator and the role prey as played by two organisms in a given interaction" xsd:string property_value: IAO:0000112 "the pink color of a medium rare piece of grilled filet mignon at its center" xsd:string property_value: IAO:0000112 "the role of being a doctor" xsd:string property_value: IAO:0000112 "the shape of this hole." xsd:string property_value: IAO:0000112 "the smell of this portion of mozzarella" xsd:string property_value: IAO:0000116 "Specifically dependent continuant doesn't have a closure axiom because the subclasses don't necessarily exhaust all possibilites. We're not sure what else will develop here, but for example there are questions such as what are promises, obligation, etc." xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/0000005", type="owl:Axiom", comment="per discussion with Barry Smith"} property_value: IAO:0000412 http://purl.obolibrary.org/obo/pato.owl property_value: IAO:0000602 "(iff (RelationalSpecificallyDependentContinuant a) (and (SpecificallyDependentContinuant a) (forall (t) (exists (b c) (and (not (SpatialRegion b)) (not (SpatialRegion c)) (not (= b c)) (not (exists (d) (and (continuantPartOfAt d b t) (continuantPartOfAt d c t)))) (specificallyDependsOnAt a b t) (specificallyDependsOnAt a c t)))))) // axiom label in BFO2 CLIF: [131-004] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/131-004", type="owl:Axiom"} property_value: IAO:0000602 "(iff (SpecificallyDependentContinuant a) (and (Continuant a) (forall (t) (if (existsAt a t) (exists (b) (and (IndependentContinuant b) (not (SpatialRegion b)) (specificallyDependsOnAt a b t))))))) // axiom label in BFO2 CLIF: [050-003] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/050-003", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000023 name: role def: "A realizable entity the manifestation of which brings about some result or end that is not essential to a continuant in virtue of the kind of thing that it is but that can be served or participated in by that kind of continuant in some kinds of natural, social or institutional contexts." [] is_a: BFO:0000017 ! realizable entity property_value: BFO:0000179 "role" xsd:string property_value: BFO:0000180 "Role" xsd:string property_value: IAO:0000112 "John’s role of husband to Mary is dependent on Mary’s role of wife to John, and both are dependent on the object aggregate comprising John and Mary as member parts joined together through the relational quality of being married." xsd:string property_value: IAO:0000112 "the priest role" xsd:string property_value: IAO:0000112 "the role of a boundary to demarcate two neighboring administrative territories" xsd:string property_value: IAO:0000112 "the role of a building in serving as a military target" xsd:string property_value: IAO:0000112 "the role of a stone in marking a property boundary" xsd:string property_value: IAO:0000112 "the role of subject in a clinical trial" xsd:string property_value: IAO:0000112 "the student role" xsd:string property_value: IAO:0000116 "BFO 2 Reference: One major family of examples of non-rigid universals involves roles, and ontologies developed for corresponding administrative purposes may consist entirely of representatives of entities of this sort. Thus ‘professor’, defined as follows,b instance_of professor at t =Def. there is some c, c instance_of professor role & c inheres_in b at t.denotes a non-rigid universal and so also do ‘nurse’, ‘student’, ‘colonel’, ‘taxpayer’, and so forth. (These terms are all, in the jargon of philosophy, phase sortals.) By using role terms in definitions, we can create a BFO conformant treatment of such entities drawing on the fact that, while an instance of professor may be simultaneously an instance of trade union member, no instance of the type professor role is also (at any time) an instance of the type trade union member role (any more than any instance of the type color is at any time an instance of the type length).If an ontology of employment positions should be defined in terms of roles following the above pattern, this enables the ontology to do justice to the fact that individuals instantiate the corresponding universals – professor, sergeant, nurse – only during certain phases in their lives." xsd:string property_value: IAO:0000600 "b is a role means: b is a realizable entity & b exists because there is some single bearer that is in some special physical, social, or institutional set of circumstances in which this bearer does not have to be& b is not such that, if it ceases to exist, then the physical make-up of the bearer is thereby changed. (axiom label in BFO2 Reference: [061-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/061-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Role x) (RealizableEntity x))) // axiom label in BFO2 CLIF: [061-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/061-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000031 name: generically dependent continuant def: "A continuant that is dependent on one or other independent continuant bearers. For every instance of A requires some instance of (an independent continuant type) B but which instance of B serves can change from time to time." [] def: "b is a generically dependent continuant = Def. b is a continuant that g-depends_on one or more other entities. (axiom label in BFO2 Reference: [074-001])" [] {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/074-001", type="owl:Axiom"} is_a: BFO:0000002 ! continuant property_value: BFO:0000179 "gdc" xsd:string property_value: BFO:0000180 "GenericallyDependentContinuant" xsd:string property_value: IAO:0000112 "The entries in your database are patterns instantiated as quality instances in your hard drive. The database itself is an aggregate of such patterns. When you create the database you create a particular instance of the generically dependent continuant type database. Each entry in the database is an instance of the generically dependent continuant type IAO: information content entity." xsd:string property_value: IAO:0000112 "the pdf file on your laptop, the pdf file that is a copy thereof on my laptop" xsd:string property_value: IAO:0000112 "the sequence of this protein molecule; the sequence that is a copy thereof in that protein molecule." xsd:string property_value: IAO:0000602 "(iff (GenericallyDependentContinuant a) (and (Continuant a) (exists (b t) (genericallyDependsOnAt a b t)))) // axiom label in BFO2 CLIF: [074-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/074-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000034 name: function is_a: BFO:0000016 ! disposition property_value: BFO:0000179 "function" xsd:string property_value: BFO:0000180 "Function" xsd:string property_value: IAO:0000112 "the function of a hammer to drive in nails" xsd:string property_value: IAO:0000112 "the function of a heart pacemaker to regulate the beating of a heart through electricity" xsd:string property_value: IAO:0000112 "the function of amylase in saliva to break down starch into sugar" xsd:string property_value: IAO:0000116 "BFO 2 Reference: In the past, we have distinguished two varieties of function, artifactual function and biological function. These are not asserted subtypes of BFO:function however, since the same function – for example: to pump, to transport – can exist both in artifacts and in biological entities. The asserted subtypes of function that would be needed in order to yield a separate monoheirarchy are not artifactual function, biological function, etc., but rather transporting function, pumping function, etc." xsd:string property_value: IAO:0000600 "A function is a disposition that exists in virtue of the bearer’s physical make-up and this physical make-up is something the bearer possesses because it came into being, either through evolution (in the case of natural biological entities) or through intentional design (in the case of artifacts), in order to realize processes of a certain sort. (axiom label in BFO2 Reference: [064-001])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/064-001", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (Function x) (Disposition x))) // axiom label in BFO2 CLIF: [064-001] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/064-001", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: BFO:0000040 name: material entity def: "An independent continuant [snap:IndependentContinuant] that is spatially extended whose identity is independent of that of other entities and can be maintained through time. Note: Material entity [snap:MaterialEntity] subsumes object [snap:Object], fiat object part [snap:FiatObjectPart], and object aggregate [snap:ObjectAggregate], which assume a three level theory of granularity, which is inadequate for some domains, such as biology." [] def: "An independent continuant that is spatially extended whose identity is independent of that of other entities and can be maintained through time." [] is_a: BFO:0000004 ! independent continuant property_value: BFO:0000179 "material" xsd:string property_value: BFO:0000180 "MaterialEntity" xsd:string property_value: IAO:0000111 "material entity" xsd:string property_value: IAO:0000111 "material entity" xsd:string property_value: IAO:0000112 "a flame" xsd:string property_value: IAO:0000112 "a forest fire" xsd:string property_value: IAO:0000112 "a human being" xsd:string property_value: IAO:0000112 "a hurricane" xsd:string property_value: IAO:0000112 "a photon" xsd:string property_value: IAO:0000112 "a puff of smoke" xsd:string property_value: IAO:0000112 "a sea wave" xsd:string property_value: IAO:0000112 "a tornado" xsd:string property_value: IAO:0000112 "an aggregate of human beings." xsd:string property_value: IAO:0000112 "an energy wave" xsd:string property_value: IAO:0000112 "an epidemic" xsd:string property_value: IAO:0000112 "Collection of random bacteria, a chair, dorsal surface of the body." xsd:string property_value: IAO:0000112 "the undetached arm of a human being" xsd:string property_value: IAO:0000116 "BFO 2 Reference: Material entities (continuants) can preserve their identity even while gaining and losing material parts. Continuants are contrasted with occurrents, which unfold themselves in successive temporal parts or phases [60" xsd:string property_value: IAO:0000116 "BFO 2 Reference: Object, Fiat Object Part and Object Aggregate are not intended to be exhaustive of Material Entity. Users are invited to propose new subcategories of Material Entity." xsd:string property_value: IAO:0000116 "BFO 2 Reference: ‘Matter’ is intended to encompass both mass and energy (we will address the ontological treatment of portions of energy in a later version of BFO). A portion of matter is anything that includes elementary particles among its proper or improper parts: quarks and leptons, including electrons, as the smallest particles thus far discovered; baryons (including protons and neutrons) at a higher level of granularity; atoms and molecules at still higher levels, forming the cells, organs, organisms and other material entities studied by biologists, the portions of rock studied by geologists, the fossils studied by paleontologists, and so on.Material entities are three-dimensional entities (entities extended in three spatial dimensions), as contrasted with the processes in which they participate, which are four-dimensional entities (entities extended also along the dimension of time).According to the FMA, material entities may have immaterial entities as parts – including the entities identified below as sites; for example the interior (or ‘lumen’) of your small intestine is a part of your body. BFO 2.0 embodies a decision to follow the FMA here." xsd:string property_value: IAO:0000119 "BFO" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/cl.owl property_value: IAO:0000412 http://purl.obolibrary.org/obo/ido.owl property_value: IAO:0000412 http://purl.obolibrary.org/obo/uberon.owl property_value: IAO:0000600 "A material entity is an independent continuant that has some portion of matter as proper or improper continuant part. (axiom label in BFO2 Reference: [019-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/019-002", type="owl:Axiom"} property_value: IAO:0000601 "every entity of which a material entity is continuant part is also a material entity. (axiom label in BFO2 Reference: [021-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/021-002", type="owl:Axiom"} property_value: IAO:0000601 "Every entity which has a material entity as continuant part is a material entity. (axiom label in BFO2 Reference: [020-002])" xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/020-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt x y t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [021-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/021-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (and (Entity x) (exists (y t) (and (MaterialEntity y) (continuantPartOfAt y x t)))) (MaterialEntity x))) // axiom label in BFO2 CLIF: [020-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/020-002", type="owl:Axiom"} property_value: IAO:0000602 "(forall (x) (if (MaterialEntity x) (IndependentContinuant x))) // axiom label in BFO2 CLIF: [019-002] " xsd:string {IAO:0010000="http://purl.obolibrary.org/obo/bfo/axiom/019-002", type="owl:Axiom"} property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl [Term] id: GO:0003674 name: molecular_function def: "A molecular process that can be carried out by the action of a single macromolecular machine, usually via direct physical interactions with other molecular entities. Function in this sense denotes an action, or activity, that a gene product (or a complex) performs. These actions are described from two distinct but related perspectives: (1) biochemical activity, and (2) role as a component in a larger system/process." [GOC:pdt] {type="owl:Axiom"} comment: Note that, in addition to forming the root of the molecular function ontology, this term is recommended for use for the annotation of gene products whose molecular function is unknown. When this term is used for annotation, it indicates that no information was available about the molecular function of the gene product annotated as of the date the annotation was made; the evidence code 'no data' (ND), is used to indicate this. Despite its name, this is not a type of 'function' in the sense typically defined by upper ontologies such as Basic Formal Ontology (BFO). It is instead a BFO:process carried out by a single gene product or complex. synonym: "molecular function" EXACT [] [Term] id: GO:0008150 name: biological_process def: "A biological process represents a specific objective that the organism is genetically programmed to achieve. Biological processes are often described by their outcome or ending state, e.g., the biological process of cell division results in the creation of two daughter cells (a divided cell) from a single parent cell. A biological process is accomplished by a particular set of molecular functions carried out by specific gene products (or macromolecular complexes), often in a highly regulated manner and in a particular temporal sequence." [GOC:pdt] {type="owl:Axiom"} comment: Note that, in addition to forming the root of the biological process ontology, this term is recommended for use for the annotation of gene products whose biological process is unknown. When this term is used for annotation, it indicates that no information was available about the biological process of the gene product annotated as of the date the annotation was made; the evidence code 'no data' (ND), is used to indicate this. synonym: "biological process" EXACT [] synonym: "physiological process" EXACT [] synonym: "single organism process" RELATED [] synonym: "single-organism process" RELATED [] xref: Wikipedia:Biological_process created_by: jl creation_date: 2012-09-19T15:05:24Z [Term] id: GO:0016301 name: kinase activity def: "Catalysis of the transfer of a phosphate group, usually from ATP, to a substrate molecule." [ISBN:0198506732] {type="owl:Axiom"} comment: Note that this term encompasses all activities that transfer a single phosphate group; although ATP is by far the most common phosphate donor, reactions using other phosphate donors are included in this term. synonym: "phosphokinase activity" EXACT [] xref: Reactome:R-HSA-6788855 "FN3KRP phosphorylates PsiAm, RibAm" {type="owl:Axiom"} xref: Reactome:R-HSA-6788867 "FN3K phosphorylates ketosamines" {type="owl:Axiom"} is_a: GO:0003674 ! molecular_function [Term] id: IAO:0000003 name: measurement unit label def: "A measurement unit label is as a label that is part of a scalar measurement datum and denotes a unit of measure." [] is_a: IAO:0000009 ! datum label property_value: IAO:0000111 "measurement unit label" xsd:string property_value: IAO:0000112 "Examples of measurement unit labels are liters, inches, weight per volume." xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "2009-03-16: provenance: a term measurement unit was\nproposed for OBI (OBI_0000176) , edited by Chris Stoeckert and\nCristian Cocos, and subsequently moved to IAO where the objective for\nwhich the original term was defined was satisfied with the definition\nof this, different, term." xsd:string property_value: IAO:0000116 "2009-03-16: review of this term done during during the OBI workshop winter 2009 and the current definition was considered acceptable for use in OBI. If there is a need to modify this definition please notify OBI." xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string [Term] id: IAO:0000005 name: objective specification def: "A directive information entity that describes an intended process endpoint. When part of a plan specification the concretization is realized in a planned process in which the bearer tries to effect the world so that the process endpoint is achieved." [] is_a: IAO:0000033 ! directive information entity property_value: IAO:0000111 "objective specification" xsd:string property_value: IAO:0000112 "In the protocol of a ChIP assay the objective specification says to identify protein and DNA interaction." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "2009-03-16: original definition when imported from OBI read: \"objective is an non realizable information entity which can serve as that proper part of a plan towards which the realization of the plan is directed.\"" xsd:string property_value: IAO:0000116 "2014-03-31: In the example of usage (\"In the protocol of a ChIP assay the objective specification says to identify protein and DNA interaction\") there is a protocol which is the ChIP assay protocol. In addition to being concretized on paper, the protocol can be concretized as a realizable entity, such as a plan that inheres in a person. The objective specification is the part that says that some protein and DNA interactions are identified. This is a specification of a process endpoint: the boundary in the process before which they are not identified and after which they are. During the realization of the plan, the goal is to get to the point of having the interactions, and participants in the realization of the plan try to do that." xsd:string property_value: IAO:0000116 "Answers the question, why did you do this experiment?" xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Barry Smith" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Jennifer Fostel" xsd:string property_value: IAO:0000118 "goal specification" xsd:string property_value: IAO:0000119 "OBI Plan and Planned Process/Roles Branch" xsd:string property_value: IAO:0000119 "OBI_0000217" xsd:string [Term] id: IAO:0000007 name: action specification def: "A directive information entity that describes an action the bearer will take." [] is_a: IAO:0000033 ! directive information entity property_value: IAO:0000112 "Pour the contents of flask 1 into flask 2" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Alan Ruttenberg" xsd:string property_value: IAO:0000119 "OBI Plan and Planned Process branch" xsd:string [Term] id: IAO:0000009 name: datum label def: "A label is a symbol that is part of some other datum and is used to either partially define the denotation of that datum or to provide a means for identifying the datum as a member of the set of data with the same label" [] is_a: IAO:0000030 ! information content entity property_value: IAO:0000111 "datum label" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000116 "http://www.golovchenko.org/cgi-bin/wnsearch?q=label#4n" xsd:string property_value: IAO:0000117 "GROUP: IAO" xsd:string property_value: IAO:0000232 "9/22/11 BP: changed the rdfs:label for this class from 'label' to 'datum label' to convey that this class is not intended to cover all kinds of labels (stickers, radiolabels, etc.), and not even all kind of textual labels, but rather the kind of labels occuring in a datum. \n" xsd:string [Term] id: IAO:0000010 name: software def: "Software is a plan specification composed of a series of instructions that can be \ninterpreted by or directly executed by a processing unit." [] is_a: IAO:0000104 ! plan specification property_value: exactMatch "http://www.ebi.ac.uk/swo/SWO_0000001" xsd:string property_value: IAO:0000111 "software" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000116 "see sourceforge tracker discussion at http://sourceforge.net/tracker/index.php?func=detail&aid=1958818&group_id=177891&atid=886178" xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000119 "GROUP: OBI" xsd:string [Term] id: IAO:0000027 name: data item def: "An information content entity that is intended to be a truthful statement about something (modulo, e.g., measurement precision or other systematic errors) and is constructed/acquired by a method which reliably tends to produce (approximately) truthful statements." [] is_a: IAO:0000030 ! information content entity relationship: SWO:0000741 IAO:0000098 ! is encoded in data format specification property_value: IAO:0000111 "data item" xsd:string property_value: IAO:0000112 "Data items include counts of things, analyte concentrations, and statistical summaries." xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "2/2/2009 Alan and Bjoern discussing FACS run output data. This is a data item because it is about the cell population. Each element records an event and is typically further composed a set of measurment data items that record the fluorescent intensity stimulated by one of the lasers." xsd:string property_value: IAO:0000116 "2009-03-16: data item deliberatly ambiguous: we merged data set and datum to be one entity, not knowing how to define singular versus plural. So data item is more general than datum." xsd:string property_value: IAO:0000116 "2009-03-16: removed datum as alternative term as datum specifically refers to singular form, and is thus not an exact synonym." xsd:string property_value: IAO:0000116 "2014-03-31: See discussion at http://odontomachus.wordpress.com/2014/03/30/aboutness-objects-propositions/" xsd:string property_value: IAO:0000116 "JAR: datum -- well, this will be very tricky to define, but maybe some \ninformation-like stuff that might be put into a computer and that is \nmeant, by someone, to denote and/or to be interpreted by some \nprocess... I would include lists, tables, sentences... I think I might \ndefer to Barry, or to Brian Cantwell Smith\n\nJAR: A data item is an approximately justified approximately true approximate belief" xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON: Jonathan Rees" xsd:string property_value: IAO:0000118 "data" xsd:string property_value: seeAlso "http://www.ontobee.org/browser/rdf.php?o=IAO&iri=http://purl.obolibrary.org/obo/IAO_0000027" xsd:string [Term] id: IAO:0000028 name: symbol def: "An information content entity that is a mark(s) or character(s) used as a conventional representation of another entity." [] is_a: IAO:0000030 ! information content entity property_value: IAO:0000111 "symbol" xsd:string property_value: IAO:0000112 "a serial number such as \"12324X\"" xsd:string property_value: IAO:0000112 "a stop sign" xsd:string property_value: IAO:0000112 "a written proper name such as \"OBI\"" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "20091104, MC: this needs work and will most probably change" xsd:string property_value: IAO:0000116 "2014-03-31: We would like to have a deeper analysis of 'mark' and 'sign' in the future (see https://github.com/information-artifact-ontology/IAO/issues/154)." xsd:string property_value: IAO:0000117 "PERSON: James A. Overton" xsd:string property_value: IAO:0000117 "PERSON: Jonathan Rees" xsd:string property_value: IAO:0000119 "based on Oxford English Dictionary" xsd:string [Term] id: IAO:0000030 name: information content entity def: "A generically dependent continuant that is about some thing." [] def: "An information content entity is an entity that is generically dependent on some artifact and stands in relation of aboutness to some entity." [] is_a: BFO:0000031 ! generically dependent continuant relationship: IAO:0000136 BFO:0000001 ! is about entity property_value: IAO:0000111 "information content entity" xsd:string property_value: IAO:0000112 "Examples of information content entites include journal articles, data, graphical layouts, and graphs." xsd:string property_value: IAO:0000112 "Examples of information content entites include journal articles, data, graphical layouts, and graphs." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "2014-03-10: The use of \"thing\" is intended to be general enough to include universals and configurations (see https://groups.google.com/d/msg/information-ontology/GBxvYZCk1oc/-L6B5fSBBTQJ)." xsd:string property_value: IAO:0000116 "information_content_entity 'is_encoded_in' some digital_entity in obi before split (040907). information_content_entity 'is_encoded_in' some physical_document in obi before split (040907).\n\nPrevious. An information content entity is a non-realizable information entity that 'is encoded in' some digital or physical entity." xsd:string property_value: IAO:0000117 "PERSON: Chris Stoeckert" xsd:string property_value: IAO:0000119 "IAO" xsd:string property_value: IAO:0000119 "OBI_0000142" xsd:string [Term] id: IAO:0000033 name: directive information entity def: "An information content entity whose concretizations indicate to their bearer how to realize them in a process." [] is_a: IAO:0000030 ! information content entity relationship: IAO:0000136 BFO:0000017 ! is about realizable entity property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "2009-03-16: provenance: a term realizable information entity was proposed for OBI (OBI_0000337) , edited by the PlanAndPlannedProcess branch. Original definition was \"is the specification of a process that can be concretized and realized by an actor\" with alternative term \"instruction\".It has been subsequently moved to IAO where the objective for which the original term was defined was satisfied with the definitionof this, different, term." xsd:string property_value: IAO:0000116 "2013-05-30 Alan Ruttenberg: What differentiates a directive information entity from an information concretization is that it can have concretizations that are either qualities or realizable entities. The concretizations that are realizable entities are created when an individual chooses to take up the direction, i.e. has the intention to (try to) realize it." xsd:string property_value: IAO:0000116 "8/6/2009 Alan Ruttenberg: Changed label from \"information entity about a realizable\" after discussions at ICBO" xsd:string property_value: IAO:0000116 "Werner pushed back on calling it realizable information entity as it isn't realizable. However this name isn't right either. An example would be a recipe. The realizable entity would be a plan, but the information entity isn't about the plan, it, once concretized, *is* the plan. -Alan" xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string [Term] id: IAO:0000037 name: dot plot def: "A dot plot is a report graph which is a graphical representation of data where each data point is represented by a single dot placed on coordinates corresponding to data point values in particular dimensions." [] is_a: IAO:0000038 ! graph property_value: IAO:0000111 "dot plot" xsd:string property_value: IAO:0000112 "Dot plot of SSC-H and FSC-H." xsd:string property_value: IAO:0000114 IAO:0000002 property_value: IAO:0000117 "person:Allyson Lister" xsd:string property_value: IAO:0000117 "person:Chris Stoeckert" xsd:string property_value: IAO:0000119 "group:OBI" xsd:string property_value: IAO:0000119 "OBI_0000123" xsd:string [Term] id: IAO:0000038 name: graph def: "A diagram that presents one or more tuples of information by mapping those tuples in to a two dimensional space in a non arbitrary way." [] is_a: IAO:0000309 ! diagram property_value: IAO:0000111 "graph" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "PERSON: Lawrence Hunter" xsd:string property_value: IAO:0000117 "person:Alan Ruttenberg" xsd:string property_value: IAO:0000117 "person:Allyson Lister" xsd:string property_value: IAO:0000119 "group:OBI" xsd:string property_value: IAO:0000119 "OBI_0000240" xsd:string [Term] id: IAO:0000064 name: algorithm def: "A plan specification which describes inputs, output of mathematical functions as well as workflow of execution for achieving an predefined objective. Algorithms are realized usually by means of implementation as computer programs for execution by automata." [] def: "A plan specification which describes the inputs and output of mathematical functions as well as workflow of execution for achieving an predefined objective. Algorithms are realized usually by means of implementation as computer programs for execution by automata." [] is_a: IAO:0000104 ! plan specification property_value: IAO:0000111 "algorithm" xsd:string property_value: IAO:0000112 "PMID: 18378114.Genomics. 2008 Mar 28. LINKGEN: A new algorithm to process data in genetic linkage studies." xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Philippe Rocca-Serra" xsd:string property_value: IAO:0000117 "PlanAndPlannedProcess Branch" xsd:string property_value: IAO:0000119 "adapted from discussion on OBI list (Matthew Pocock, Christian Cocos, Alan Ruttenberg)" xsd:string property_value: IAO:0000119 "IAO" xsd:string property_value: IAO:0000119 "OBI_0000270" xsd:string [Term] id: IAO:0000078 name: curation status specification def: "The curation status of the term. The allowed values come from an enumerated list of predefined terms. See the specification of these instances for more detailed definitions of each enumerated value." [] is_a: IAO:0000102 ! data about an ontology part property_value: IAO:0000111 "curation status specification" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "Better to represent curation as a process with parts and then relate labels to that process (in IAO meeting)" xsd:string property_value: IAO:0000117 "PERSON:Bill Bug" xsd:string property_value: IAO:0000119 "GROUP:OBI:" xsd:string property_value: IAO:0000119 "OBI_0000266" xsd:string [Term] id: IAO:0000096 name: source code module def: "A source code module is a directive information entity that specifies, using a programming language, some algorithm." [] is_a: IAO:0000033 ! directive information entity property_value: IAO:0000111 "source code module" xsd:string property_value: IAO:0000112 "The written source code that implements part of an algorithm. Test - if you know that it was written in a specific language, then it can be source code module. We mean here, roughly, the wording of a document such as a perl script." xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "person:Alan Ruttenberg" xsd:string property_value: IAO:0000117 "person:Chris Stoeckert" xsd:string property_value: IAO:0000119 "group:OBI" xsd:string property_value: IAO:0000119 "OBI_0000039" xsd:string [Term] id: IAO:0000098 name: data format specification def: "A data format specification is the information content borne by the document published defining the specification.\nExample: The ISO document specifying what encompasses an XML document; The instructions in a XSD file" [] is_a: IAO:0000033 ! directive information entity property_value: IAO:0000111 "data format specification" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000116 "2009-03-16: provenance: term imported from OBI_0000187, which had original definition \"A data format specification is a plan which organizes\ninformation. Example: The ISO document specifying what encompasses an\nXML document; The instructions in a XSD file\"" xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PlanAndPlannedProcess Branch" xsd:string property_value: IAO:0000119 "OBI branch derived" xsd:string property_value: IAO:0000119 "OBI_0000187" xsd:string [Term] id: IAO:0000100 name: data set def: "A data item that is an aggregate of other data items of the same type that have something in common. Averages and distributions can be determined for data sets." [] is_a: IAO:0000027 ! data item property_value: IAO:0000111 "data set" xsd:string property_value: IAO:0000112 "Intensity values in a CEL file or from multiple CEL files comprise a data set (as opposed to the CEL files themselves)." xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "2009/10/23 Alan Ruttenberg. The intention is that this term represent collections of like data. So this isn't for, e.g. the whole contents of a cel file, which includes parameters, metadata etc. This is more like java arrays of a certain rather specific type" xsd:string property_value: IAO:0000116 "2014-05-05: Data sets are aggregates and thus must include two or more data items. We have chosen not to add logical axioms to make this restriction." xsd:string property_value: IAO:0000117 "person:Allyson Lister" xsd:string property_value: IAO:0000117 "person:Chris Stoeckert" xsd:string property_value: IAO:0000119 "group:OBI" xsd:string property_value: IAO:0000119 "OBI_0000042" xsd:string [Term] id: IAO:0000101 name: image def: "An image is an affine projection to a two dimensional surface, of measurements of some quality of an entity or entities repeated at regular intervals across a spatial range, where the measurements are represented as color and luminosity on the projected on surface." [] is_a: IAO:0000308 ! figure property_value: IAO:0000111 "image" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "person:Alan Ruttenberg" xsd:string property_value: IAO:0000117 "person:Allyson" xsd:string property_value: IAO:0000117 "person:Chris Stoeckert" xsd:string property_value: IAO:0000119 "group:OBI" xsd:string property_value: IAO:0000119 "OBI_0000030" xsd:string [Term] id: IAO:0000102 name: data about an ontology part def: "Data about an ontology part is a data item about a part of an ontology, for example a term" [] is_a: IAO:0000027 ! data item property_value: IAO:0000111 "data about an ontology part" xsd:string property_value: IAO:0000117 "Person:Alan Ruttenberg" xsd:string [Term] id: IAO:0000104 name: plan specification def: "A directive information entity with action specifications and objective specifications as parts that, when concretized, is realized in a process in which the bearer tries to achieve the objectives by taking the actions specified." [] def: "A directive information entity with action specifications and objective specifications as parts, and that may be concretized as a realizable entity that, if realized, is realized in a process in which the bearer tries to achieve the objectives by taking the actions specified." [] comment: 2/3/2009 Comment from OBI review.\n\nAction specification not well enough specified.\nConditional specification not well enough specified.\nQuestion whether all plan specifications have objective specifications.\n\nRequest that IAO either clarify these or change definitions not to use them is_a: IAO:0000033 ! directive information entity relationship: BFO:0000051 IAO:0000005 ! has part objective specification relationship: BFO:0000051 IAO:0000007 ! has part action specification property_value: IAO:0000111 "plan specification" xsd:string property_value: IAO:0000112 "PMID: 18323827.Nat Med. 2008 Mar;14(3):226.New plan proposed to help resolve conflicting medical advice." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "2009-03-16: provenance: a term a plan was proposed for OBI (OBI_0000344) , edited by the PlanAndPlannedProcess branch. Original definition was \" a plan is a specification of a process that is realized by an actor to achieve the objective specified as part of the plan\". It has been subsequently moved to IAO where the objective for which the original term was defined was satisfied with the definitionof this, different, term." xsd:string property_value: IAO:0000116 "2014-03-31: A plan specification can have other parts, such as conditional specifications." xsd:string property_value: IAO:0000116 "2022-01-16 Updated definition to that proposed by Clint Dowloand, IAO Issue 231." xsd:string property_value: IAO:0000116 "Alternative previous definition: a plan is a set of instructions that specify how an objective should be achieved" xsd:string property_value: IAO:0000117 "Alan Ruttenberg" xsd:string property_value: IAO:0000117 "Clint Dowland" xsd:string property_value: IAO:0000119 "OBI Plan and Planned Process branch" xsd:string property_value: IAO:0000119 "OBI_0000344" xsd:string property_value: seeAlso "https://github.com/information-artifact-ontology/IAO/issues/231#issuecomment-1010455131" xsd:string [Term] id: IAO:0000129 name: version name name: version number def: "A version number is an information content entity which is a sequence of characters borne by part of each of a class of manufactured products or its packaging and indicates its order within a set of other products having the same name." [] is_a: IAO:0000028 ! symbol property_value: IAO:0000111 "version number" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000116 "Note: we feel that at the moment we are happy with a general version number, and that we will subclass as needed in the future. For example, see 7. genome sequence version" xsd:string property_value: IAO:0000117 "GROUP: IAO" xsd:string [Term] id: IAO:0000178 name: material information bearer def: "A material entity in which a concretization of an information content entity inheres." [] is_a: BFO:0000040 ! material entity property_value: IAO:0000111 "material information bearer" xsd:string property_value: IAO:0000112 "a brain" xsd:string property_value: IAO:0000112 "a hard drive" xsd:string property_value: IAO:0000112 "A page of a paperback novel with writing on it. The paper itself is a material information bearer, the pattern of ink is the information carrier." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "GROUP: IAO" xsd:string [Term] id: IAO:0000179 name: histogram def: "A histogram is a report graph which is a statistical description of a\ndistribution in terms of occurrence frequencies of different event classes." [] is_a: IAO:0000038 ! graph property_value: IAO:0000111 "histogram" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON:Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON:James Malone" xsd:string property_value: IAO:0000117 "PERSON:Melanie Courtot" xsd:string property_value: IAO:0000119 "GROUP:OBI" xsd:string [Term] id: IAO:0000180 name: heatmap def: "A heatmap is a report graph which is a graphical representation of data\nwhere the values taken by a variable(s) are shown as colors in a\ntwo-dimensional map." [] is_a: IAO:0000038 ! graph property_value: IAO:0000111 "heatmap" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON:Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON:James Malone" xsd:string property_value: IAO:0000117 "PERSON:Melanie Courtot" xsd:string property_value: IAO:0000119 "GROUP:OBI" xsd:string [Term] id: IAO:0000183 name: dendrogram def: "A dendrogram is a report graph which is a tree diagram\nfrequently used to illustrate the arrangement of the clusters produced by a\nclustering algorithm." [] is_a: IAO:0000038 ! graph property_value: IAO:0000111 "dendrogram" xsd:string property_value: IAO:0000112 "Dendrograms are often used in computational biology to\nillustrate the clustering of genes." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON:Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON:James Malone" xsd:string property_value: IAO:0000117 "PERSON:Melanie Courtot" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/Dendrogram" xsd:string [Term] id: IAO:0000184 name: scatter plot def: "A scatterplot is a graph which uses Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis." [] is_a: IAO:0000038 ! graph property_value: IAO:0000111 "scatter plot" xsd:string property_value: IAO:0000112 "Comparison of gene expression values in two samples can be displayed in a scatter plot" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON:Chris Stoeckert" xsd:string property_value: IAO:0000117 "PERSON:James Malone" xsd:string property_value: IAO:0000117 "PERSON:Melanie Courtot" xsd:string property_value: IAO:0000118 "scattergraph" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/Scatterplot" xsd:string [Term] id: IAO:0000225 name: obsolescence reason specification def: "The reason for which a term has been deprecated. The allowed values come from an enumerated list of predefined terms. See the specification of these instances for more detailed definitions of each enumerated value." [] is_a: IAO:0000102 ! data about an ontology part property_value: IAO:0000111 "obsolescence reason specification" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "The creation of this class has been inspired in part by Werner Ceusters' paper, Applying evolutionary terminology auditing to the Gene Ontology." xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string [Term] id: IAO:0000308 name: figure def: "An information content entity consisting of a two dimensional arrangement of information content entities such that the arrangement itself is about something." [] is_a: IAO:0000030 ! information content entity property_value: IAO:0000111 "figure" xsd:string property_value: IAO:0000112 "Any picture, diagram or table" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "PERSON: Lawrence Hunter" xsd:string [Term] id: IAO:0000309 name: diagram def: "A figure that expresses one or more propositions" [] is_a: IAO:0000308 ! figure property_value: IAO:0000111 "diagram" xsd:string property_value: IAO:0000112 "A molecular structure ribbon cartoon showing helices, turns and sheets and their relations to each other in space." xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "PERSON: Lawrence Hunter" xsd:string [Term] id: IAO:0000310 name: document def: "A collection of information content entities intended to be understood together as a whole" [] is_a: IAO:0000030 ! information content entity property_value: IAO:0000111 "document" xsd:string property_value: IAO:0000112 "A journal article, patent application, laboratory notebook, or a book" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "PERSON: Lawrence Hunter" xsd:string [Term] id: IAO:0000409 name: denotator type def: "A denotator type indicates how a term should be interpreted from an ontological perspective." [] is_a: IAO:0000102 ! data about an ontology part property_value: IAO:0000111 "denotator type" xsd:string property_value: IAO:0000112 "The Basic Formal Ontology ontology makes a distinction between Universals and defined classes, where the formal are \"natural kinds\" and the latter arbitrary collections of entities." xsd:string property_value: IAO:0000117 "Alan Ruttenberg" xsd:string property_value: IAO:0000119 "Barry Smith, Werner Ceusters" xsd:string [Term] id: NCBITaxon:10239 name: Viruses is_a: OBI:0100026 ! organism property_value: IAO:0000111 "Viruses" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:117571 name: Euteleostomi is_a: NCBITaxon:7742 ! Vertebrata property_value: IAO:0000111 "Euteleostomi" xsd:string property_value: IAO:0000118 "bony vertebrates" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:2 name: Bacteria is_a: OBI:0100026 ! organism property_value: IAO:0000111 "Bacteria" xsd:string property_value: IAO:0000118 "eubacteria" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:2157 name: Archaea is_a: OBI:0100026 ! organism property_value: IAO:0000111 "Archaea" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:2759 name: Eukaryota is_a: OBI:0100026 ! organism property_value: IAO:0000111 "Eukaryota" xsd:string property_value: IAO:0000118 "eucaryotes" xsd:string property_value: IAO:0000118 "eukaryotes" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:314146 name: Euarchontoglires is_a: NCBITaxon:40674 ! Mammalia property_value: IAO:0000111 "Euarchontoglires" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:32523 name: Tetrapoda is_a: NCBITaxon:117571 ! Euteleostomi property_value: IAO:0000111 "Tetrapoda" xsd:string property_value: IAO:0000118 "tetrapods" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:32524 name: Amniota is_a: NCBITaxon:32523 ! Tetrapoda property_value: IAO:0000111 "Amniota" xsd:string property_value: IAO:0000118 "amniotes" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:33154 name: Opisthokonta is_a: NCBITaxon:2759 ! Eukaryota property_value: IAO:0000111 "Opisthokonta" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:33208 name: Metazoa is_a: NCBITaxon:33154 ! Opisthokonta property_value: IAO:0000111 "Metazoa" xsd:string property_value: IAO:0000118 "metazoans" xsd:string property_value: IAO:0000118 "multicellular animals" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:33213 name: Bilateria is_a: NCBITaxon:33208 ! Metazoa property_value: IAO:0000111 "Bilateria" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:40674 name: Mammalia is_a: NCBITaxon:32524 ! Amniota property_value: IAO:0000111 "Mammalia" xsd:string property_value: IAO:0000118 "mammals" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:7742 name: Vertebrata is_a: NCBITaxon:33213 ! Bilateria property_value: IAO:0000111 "Vertebrata " xsd:string property_value: IAO:0000118 "Vertebrata" xsd:string property_value: IAO:0000118 "vertebrates" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: NCBITaxon:9606 name: Homo sapiens is_a: NCBITaxon:314146 ! Euarchontoglires property_value: IAO:0000111 "Homo sapiens" xsd:string property_value: IAO:0000118 "human" xsd:string property_value: IAO:0000118 "human being" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/ncbitaxon.owl [Term] id: OBI:0000011 name: planned process def: "A process that realizes a plan which is the concretization of a plan specification." [] is_a: BFO:0000015 ! process property_value: IAO:0000111 "planned process" xsd:string property_value: IAO:0000111 "planned process" xsd:string property_value: IAO:0000112 "Injecting mice with a vaccine in order to test its efficacy" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "'Plan' includes a future direction sense. That can be problematic if plans are changed during their execution. There are however implicit contingencies for protocols that an agent has in his mind that can be considered part of the plan, even if the agent didn't have them in mind before. Therefore, a planned process can diverge from what the agent would have said the plan was before executing it, by adjusting to problems encountered during execution (e.g. choosing another reagent with equivalent properties, if the originally planned one has run out.)" xsd:string property_value: IAO:0000116 "We are only considering successfully completed planned processes. A plan may be modified, and details added during execution. For a given planned process, the associated realized plan specification is the one encompassing all changes made during execution. This means that all processes in which an agent acts towards achieving some \nobjectives is a planned process." xsd:string property_value: IAO:0000117 "Bjoern Peters" xsd:string property_value: IAO:0000119 "branch derived" xsd:string property_value: IAO:0000232 "6/11/9: Edited at workshop. Used to include: is initiated by an agent" xsd:string property_value: IAO:0000232 "This class merges the previously separated objective driven process and planned process, as they the separation proved hard to maintain. (1/22/09, branch call)" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/obi.owl [Term] id: OBI:0000014 name: regulator role def: "a regulatory role involved with making and/or enforcing relevant legislation and governmental orders" [] is_a: OBI:0000017 ! regulatory role property_value: IAO:0000111 "regulator role" xsd:string property_value: IAO:0000112 "Fact sheet - Regulating the companies The role of the regulator. Ofwat is the economic regulator of the water and sewerage industry in England and Wales. http://www.ofwat.gov.uk/aptrix/ofwat/publish.nsf/Content/roleofregulator_factsheet170805" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Person:Jennifer Fostel" xsd:string property_value: IAO:0000118 "regulator" xsd:string property_value: IAO:0000119 "OBI" xsd:string [Term] id: OBI:0000017 name: regulatory role def: "a role which inheres in material entities and is realized in the processes of making, enforcing or being defined by legislation or orders issued by a governmental body." [] is_a: BFO:0000023 ! role property_value: IAO:0000111 "regulatory role" xsd:string property_value: IAO:0000112 "Regulatory agency, Ethics committee, Approval letter; example: Browse these EPA Regulatory Role subtopics http://www.epa.gov/ebtpages/enviregulatoryrole.html Feb 29, 2008" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "GROUP: Role branch" xsd:string property_value: IAO:0000119 "OBI, CDISC" xsd:string property_value: IAO:0000232 "govt agents responsible for creating regulations; proxies for enforcing regulations. CDISC definition: regulatory authorities. Bodies having the power to regulate. NOTE: In the ICH GCP guideline the term includes the authorities that review submitted clinical data and those that conduct inspections. These bodies are sometimes referred to as competent" xsd:string [Term] id: OBI:0000018 name: material supplier role def: "a role realized through the process of supplying materials such as animal subjects, reagents or other materials used in an investigation." [] is_a: OBI:0000947 ! service provider role property_value: IAO:0000111 "material supplier role" xsd:string property_value: IAO:0000112 "Jackson Labs is an organization which provide mice as experimental material" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000116 "Supplier role is a special kind of service, e.g. biobank" xsd:string property_value: IAO:0000117 "PERSON:Jennifer Fostel" xsd:string property_value: IAO:0000118 "material provider role" xsd:string property_value: IAO:0000118 "supplier" xsd:string [Term] id: OBI:0000023 name: classified data set def: "A data set that is produced as the output of a class prediction data transformation and consists of a data set with assigned class labels." [] is_a: IAO:0000100 ! data set relationship: OBI:0000312 OBI:0000663 ! is_specified_output_of class prediction data transformation property_value: IAO:0000111 "classified data set" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "PERSON: James Malone" xsd:string property_value: IAO:0000117 "PERSON: Monnie McGee" xsd:string property_value: IAO:0000118 "data set with assigned class labels" xsd:string [Term] id: OBI:0000047 name: processed material def: "Is a material entity that is created or changed during material processing." [] is_a: BFO:0000040 ! material entity intersection_of: BFO:0000040 ! material entity intersection_of: OBI:0000312 OBI:0000094 ! is_specified_output_of material processing relationship: OBI:0000312 OBI:0000094 ! is_specified_output_of material processing property_value: IAO:0000111 "processed material" xsd:string property_value: IAO:0000112 "Examples include gel matrices, filter paper, parafilm and buffer solutions, mass spectrometer, tissue samples" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string [Term] id: OBI:0000094 name: material processing def: "A planned process which results in physical changes in a specified input material" [] is_a: OBI:0000011 ! planned process relationship: OBI:0000293 BFO:0000040 {all_only="true"} ! has_specified_input material entity relationship: OBI:0000299 OBI:0000047 ! has_specified_output processed material relationship: OBI:0000417 OBI:0000456 ! achieves_planned_objective material transformation objective property_value: IAO:0000111 "material processing" xsd:string property_value: IAO:0000112 "A cell lysis, production of a cloning vector, creating a buffer." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Frank Gibson" xsd:string property_value: IAO:0000117 "PERSON: Jennifer Fostel" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca Serra" xsd:string property_value: IAO:0000118 "material transformation" xsd:string property_value: IAO:0000119 "OBI branch derived" xsd:string [Term] id: OBI:0000112 name: specimen role def: "a role borne by a material entity that is gained during a specimen collection process and that can be realized by use of the specimen in an investigation" [] is_a: BFO:0000023 ! role property_value: IAO:0000111 "specimen role" xsd:string property_value: IAO:0000112 "liver section; a portion of a culture of cells; a nemotode or other animal once no longer a subject (generally killed); portion of blood from a patient." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "22Jun09. The definition includes whole organisms, and can include a human. The link between specimen role and study subject role has been removed. A specimen taken as part of a case study is not considered to be a population representative, while a specimen taken as representing a population, e.g. person taken from a cohort, blood specimen taken from an animal) would be considered a population representative and would also bear material sample role." xsd:string property_value: IAO:0000116 "blood taken from animal: animal continues in study, whereas blood has role specimen.\nsomething taken from study subject, leaves the study and becomes the specimen." xsd:string property_value: IAO:0000116 "Note: definition is in specimen creation objective which is defined as an objective to obtain and store a material entity for potential use as an input during an investigation." xsd:string property_value: IAO:0000116 "parasite example\n- when parasite in people we study people, people are subjects and parasites are specimen\n- when parasite extracted, they become subject in the following study\nspecimen can later be subject." xsd:string property_value: IAO:0000117 "GROUP: Role Branch" xsd:string property_value: IAO:0000119 "OBI" xsd:string property_value: IAO:0000233 https://github.com/obi-ontology/obi/issues/1013 [Term] id: OBI:0000245 name: organization def: "An entity that can bear roles, has members, and has a set of organization rules. Members of organizations are either organizations themselves or individual people. Members can bear specific organization member roles that are determined in the organization rules. The organization rules also determine how decisions are made on behalf of the organization by the organization members." [] is_a: BFO:0000040 ! material entity property_value: IAO:0000111 "organization" xsd:string property_value: IAO:0000112 "PMID: 16353909.AAPS J. 2005 Sep 22;7(2):E274-80. Review. The joint food and agriculture organization of the United Nations/World Health Organization Expert Committee on Food Additives and its role in the evaluation of the safety of veterinary drug residues in foods." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "BP: The definition summarizes long email discussions on the OBI developer, roles, biomaterial and denrie branches. It leaves open if an organization is a material entity or a dependent continuant, as no consensus was reached on that. The current placement as material is therefore temporary, in order to move forward with development. Here is the entire email summary, on which the definition is based:\n\n1) there are organization_member_roles (president, treasurer, branch\neditor), with individual persons as bearers\n\n2) there are organization_roles (employer, owner, vendor, patent holder)\n\n3) an organization has a charter / rules / bylaws, which specify what roles\nthere are, how they should be realized, and how to modify the\ncharter/rules/bylaws themselves.\n\nIt is debatable what the organization itself is (some kind of dependent\ncontinuant or an aggregate of people). This also determines who/what the\nbearer of organization_roles' are. My personal favorite is still to define\norganization as a kind of 'legal entity', but thinking it through leads to\nall kinds of questions that are clearly outside the scope of OBI.\n\nInterestingly enough, it does not seem to matter much where we place\norganization itself, as long as we can subclass it (University, Corporation,\nGovernment Agency, Hospital), instantiate it (Affymetrix, NCBI, NIH, ISO,\nW3C, University of Oklahoma), and have it play roles.\n\nThis leads to my proposal: We define organization through the statements 1 -\n3 above, but without an 'is a' statement for now. We can leave it in its\ncurrent place in the is_a hierarchy (material entity) or move it up to\n'continuant'. We leave further clarifications to BFO, and close this issue\nfor now." xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca-Serra" xsd:string property_value: IAO:0000117 "PERSON: Susanna Sansone" xsd:string property_value: IAO:0000119 "GROUP: OBI" xsd:string [Term] id: OBI:0000450 name: regulatory agency def: "A regulatory agency is a organization that has responsibility over or for the legislation (acts and regulations) for a given sector of the government." [] is_a: OBI:0000245 ! organization intersection_of: OBI:0000245 ! organization intersection_of: RO:0000087 OBI:0000014 ! has role regulator role relationship: RO:0000087 OBI:0000014 ! has role regulator role property_value: IAO:0000111 "regulatory agency" xsd:string property_value: IAO:0000112 "The US Environmental Protection Agency" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "GROUP: OBI Biomaterial Branch" xsd:string property_value: IAO:0000119 "WEB: en.wikipedia.org/wiki/Regulator" xsd:string [Term] id: OBI:0000456 name: material transformation objective def: "an objective specifiction that creates an specific output object from input materials." [] is_a: IAO:0000005 ! objective specification property_value: IAO:0000111 "material transformation objective" xsd:string property_value: IAO:0000112 "The objective to create a mouse infected with LCM virus. The objective to create a defined solution of PBS." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Frank Gibson" xsd:string property_value: IAO:0000117 "PERSON: Jennifer Fostel" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca-Serra" xsd:string property_value: IAO:0000118 "artifact creation objective" xsd:string property_value: IAO:0000119 "GROUP: OBI PlanAndPlannedProcess Branch" xsd:string [Term] id: OBI:0000457 name: manufacturing def: "Manufacturing is a process with the intent to produce a processed material which will have a function for future use. A person or organization (having manufacturer role) is a participant in this process" [] is_a: OBI:0000094 ! material processing relationship: OBI:0000293 BFO:0000040 ! has_specified_input material entity relationship: OBI:0000417 OBI:0000458 ! achieves_planned_objective manufacturing objective property_value: IAO:0000111 "manufacturing" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "Manufacturing implies reproducibility and responsibility AR" xsd:string property_value: IAO:0000116 "This includes a single scientist making a processed material for personal use." xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Frank Gibson" xsd:string property_value: IAO:0000117 "PERSON: Jennifer Fostel" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca-Serra" xsd:string property_value: IAO:0000119 "GROUP: OBI PlanAndPlannedProcess Branch" xsd:string [Term] id: OBI:0000458 name: manufacturing objective def: "is the objective to manufacture a material of a certain function (device)" [] is_a: OBI:0000456 ! material transformation objective property_value: IAO:0000111 "manufacturing objective" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Frank Gibson" xsd:string property_value: IAO:0000117 "PERSON: Jennifer Fostel" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca-Serra" xsd:string property_value: IAO:0000119 "GROUP: OBI PlanAndPlannedProcess Branch" xsd:string [Term] id: OBI:0000571 name: manufacturer role def: "Manufacturer role is a role which inheres in a person or organization and which is realized by a manufacturing process." [] is_a: BFO:0000023 ! role relationship: BFO:0000054 OBI:0000457 {all_only="true"} ! realized in manufacturing property_value: IAO:0000111 "manufacturer role" xsd:string property_value: IAO:0000112 "With respect to The Accuri C6 Flow Cytometer System, the organization Accuri bears the role manufacturer role. With respect to a transformed line of tissue culture cells derived by a specific lab, the lab whose personnel isolated the cll line bears the role manufacturer role. With respect to a specific antibody produced by an individual scientist, the scientist who purifies, characterizes and distributes the anitbody bears the role manufacturer role." xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "GROUP: Role Branch" xsd:string property_value: IAO:0000119 "OBI" xsd:string [Term] id: OBI:0000648 name: clustered data set def: "A data set that is produced as the output of a class discovery data transformation and consists of a data set with assigned discovered class labels." [] is_a: IAO:0000100 ! data set relationship: OBI:0000312 OBI:0200175 ! is_specified_output_of class discovery data transformation property_value: IAO:0000111 "clustered data set" xsd:string property_value: IAO:0000112 "A clustered data set is the output of a K means clustering data transformation" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "PERSON: James Malone" xsd:string property_value: IAO:0000117 "PERSON: Monnie McGee" xsd:string property_value: IAO:0000118 "data set with assigned discovered class labels" xsd:string property_value: IAO:0000232 "AR thinks could be a data item instead" xsd:string [Term] id: OBI:0000659 name: specimen collection process def: "A planned process with the objective of collecting a specimen." [] is_a: OBI:0000011 ! planned process intersection_of: OBI:0000011 ! planned process intersection_of: OBI:0000417 OBI:0000684 ! achieves_planned_objective specimen collection objective relationship: OBI:0000293 BFO:0000040 ! has_specified_input material entity relationship: OBI:0000299 OBI:0100051 ! has_specified_output specimen relationship: OBI:0000417 OBI:0000684 ! achieves_planned_objective specimen collection objective property_value: IAO:0000111 "specimen collection process" xsd:string property_value: IAO:0000112 "drawing blood from a patient for analysis, collecting a piece of a plant for depositing in a herbarium, buying meat from a butcher in order to measure its protein content in an investigation" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "label changed to 'specimen collection process' on 10/27/2014, details see tracker:\nhttp://sourceforge.net/p/obi/obi-terms/716/" xsd:string property_value: IAO:0000116 "Note: definition is in specimen creation objective which is defined as an objective to obtain and store a material entity for potential use as an input during an investigation." xsd:string property_value: IAO:0000116 "Philly2013: A specimen collection can have as part a material entity acquisition, such as ordering from a bank. The distinction is that specimen collection necessarily involves the creation of a specimen role. However ordering cell lines cells from ATCC for use in an investigation is NOT a specimen collection, because the cell lines already have a specimen role." xsd:string property_value: IAO:0000116 "Philly2013: The specimen_role for the specimen is created during the specimen collection process." xsd:string property_value: IAO:0000117 "Bjoern Peters" xsd:string property_value: IAO:0000118 "specimen collection" xsd:string property_value: IAO:0000232 "5/31/2012: This process is not necessarily an acquisition, as specimens may be collected from materials already in posession" xsd:string property_value: IAO:0000232 "6/9/09: used at workshop" xsd:string [Term] id: OBI:0000663 name: class prediction data transformation def: "A class prediction data transformation (sometimes called supervised classification) is a data transformation that has objective class prediction." [] is_a: OBI:0200000 ! data transformation property_value: IAO:0000111 "class prediction data transformation" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "supervised classification data transformation" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0000684 name: specimen collection objective def: "A objective specification to obtain a material entity for potential use as an input during an investigation." [] is_a: IAO:0000005 ! objective specification property_value: IAO:0000111 "specimen collection objective" xsd:string property_value: IAO:0000112 "The objective to collect bits of excrement in the rainforest. The objective to obtain a blood sample from a patient." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Bjoern Peters" xsd:string property_value: IAO:0000119 "Bjoern Peters" xsd:string [Term] id: OBI:0000700 name: support vector machine def: "A support vector machine is a data transformation with a class prediction objective based on the construction of a separating hyperplane that maximizes the margin between two data sets of vectors in n-dimensional space." [] is_a: OBI:0000663 ! class prediction data transformation relationship: OBI:0000417 OBI:0200179 ! achieves_planned_objective class prediction objective property_value: IAO:0000111 "support vector machine" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Ryan Brinkman" xsd:string property_value: IAO:0000118 "SVM" xsd:string property_value: IAO:0000119 "PERSON: Ryan Brinkman" xsd:string [Term] id: OBI:0000704 name: decision tree induction objective def: "A decision tree induction objective is a data transformation objective in which a tree-like graph of edges and nodes is created and from which the selection of each branch requires that some type of logical decision is made." [] is_a: OBI:0200166 ! data transformation objective property_value: IAO:0000111 "decision tree induction objective" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "James Malone" xsd:string [Term] id: OBI:0000707 name: decision tree building data transformation def: "A decision tree building data transformation is a data transformation that has objective decision tree induction." [] is_a: OBI:0200000 ! data transformation relationship: OBI:0000417 OBI:0000704 ! achieves_planned_objective decision tree induction objective property_value: IAO:0000111 "decision tree building data transformation" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0000713 name: GenePattern software def: "a software that provides access to more than 100 tools for gene expression analysis, proteomics, SNP analysis and common data processing tasks." [] is_a: IAO:0000010 ! software property_value: IAO:0000111 "GenePattern software" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Person:Helen Parkinson" xsd:string property_value: IAO:0000119 "WEB: http://www.broadinstitute.org/cancer/software/genepattern/" xsd:string [Term] id: OBI:0000726 name: peak matching def: "Peak matching is a data transformation performed on a dataset of a graph of ordered data points (e.g. a spectrum) with the objective of pattern matching local maxima above a noise threshold" [] is_a: OBI:0200000 ! data transformation property_value: IAO:0000111 "peak matching" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Ryan Brinkman" xsd:string property_value: IAO:0000119 "PERSON: Ryan Brinkman" xsd:string [Term] id: OBI:0000727 name: k-nearest neighbors def: "A k-nearest neighbors is a data transformation which achieves a class discovery or partitioning objective, in which an input data object with vector y is assigned to a class label based upon the k closest training data set points to y; where k is the largest value that class label is assigned." [] is_a: APOLLO_SV:00000796 ! dataset creating is_a: OBI:0200171 ! partitioning data transformation is_a: OBI:0200175 ! class discovery data transformation relationship: OBI:0000299 OBI:0000648 ! has_specified_output clustered data set relationship: OBI:0000417 OBI:0200178 ! achieves_planned_objective class discovery objective property_value: IAO:0000111 "k-nearest neighbors" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "k-NN" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0000749 name: CART def: "A CART (classification and regression trees) is a data transformation method for producing a classification or regression model with a tree-based structure." [] is_a: OBI:0000707 ! decision tree building data transformation property_value: IAO:0000111 "CART" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "classification and regression trees" xsd:string property_value: IAO:0000119 "BOOK: David J. Hand, Heikki Mannila and Padhraic Smyth (2001) Principles of Data Mining." xsd:string [Term] id: OBI:0000792 name: statistical model validation def: "A data transformation which assesses how the results of a statistical analysis will generalize to an independent data set." [] is_a: OBI:0200171 ! partitioning data transformation relationship: OBI:0000417 OBI:0200188 ! achieves_planned_objective cross validation objective property_value: IAO:0000111 "statistical model validation" xsd:string property_value: IAO:0000112 "Using the expression levels of 20 proteins to predict whether a cancer patient will respond to a drug. A practical goal would be to determine which subset of the 20 features should be used to produce the best predictive model. - wikipedia" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Helen Parkinson" xsd:string property_value: IAO:0000119 "http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29" xsd:string [Term] id: OBI:0000835 name: manufacturer def: "A person or organization that has a manufacturer role" [] is_a: BFO:0000040 ! material entity relationship: RO:0000087 OBI:0000571 ! has role manufacturer role property_value: IAO:0000111 "manufacturer" xsd:string property_value: IAO:0000114 IAO:0000123 [Term] id: OBI:0000947 name: service provider role def: "is a role which inheres in a person or organization and is realized in in a planned process which provides access to training, materials or execution of protocols for an organization or person" [] is_a: BFO:0000023 ! role relationship: BFO:0000054 OBI:0000011 {all_only="true"} ! realized in planned process property_value: IAO:0000111 "service provider role" xsd:string property_value: IAO:0000112 "Jackson Lab provides experimental animals, EBI provides training on databases, a core facility provides access to a DNA sequencer." xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "PERSON:Helen Parkinson" xsd:string [Term] id: OBI:0000953 name: processed specimen def: "A specimen that has been intentionally physically modified." [] comment: A tissue sample that has been sliced and stained for a histology study. is_a: OBI:0000047 ! processed material is_a: OBI:0100051 ! specimen intersection_of: OBI:0000047 ! processed material intersection_of: OBI:0100051 ! specimen property_value: IAO:0000111 "processed specimen" xsd:string property_value: IAO:0000112 "A tissue sample that has been sliced and stained for a histology study.\nA blood specimen that has been centrifuged to obtain the white blood cells." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Bjoern Peters" xsd:string property_value: IAO:0000119 "Bjoern Peters" xsd:string [Term] id: OBI:0000963 name: categorical label def: "A label that is part of a categorical datum and that indicates the value of the data item on the categorical scale." [] is_a: IAO:0000009 ! datum label property_value: IAO:0000111 "categorical label" xsd:string property_value: IAO:0000112 "The labels 'positive' vs. 'negative', or 'left handed', 'right handed', 'ambidexterous', or 'strongly binding', 'weakly binding' , 'not binding', or '+++', '++', '+', '-' etc. form scales of categorical labels. " xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Bjoern Peters" xsd:string property_value: IAO:0000119 "Bjoern Peters" xsd:string [Term] id: OBI:0001000 name: questionnaire def: "A document with a set of printed or written questions with a choice of answers, devised for the purposes of a survey or statistical study." [] is_a: IAO:0000310 ! document property_value: IAO:0000111 "questionnaire" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000116 "JT: It plays a role in collecting data that could be fleshed out more; but I'm thinking it is, in itself, an edited document. \nJZ: based on textual definition of edited document, it can be defined as N&S. I prefer to leave questionnaire as a document now. We can add more restrictions in the future and use that to determine it is an edited document or not. " xsd:string property_value: IAO:0000116 "Need to clarify if this is a document or a directive information entity (or what their connection is))" xsd:string property_value: IAO:0000117 "PERSON: Jessica Turner" xsd:string property_value: IAO:0000119 "Merriam-Webster" xsd:string [Term] id: OBI:0001930 name: categorical value specification def: "A value specification that is specifies one category out of a fixed number of nominal categories" [] is_a: OBI:0001933 ! value specification property_value: IAO:0000111 "categorical value specification" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "PERSON:Bjoern Peters" xsd:string [Term] id: OBI:0001933 name: value specification def: "An information content entity that specifies a value within a classification scheme or on a quantitative scale." [] is_a: IAO:0000030 ! information content entity property_value: IAO:0000111 "value specification" xsd:string property_value: IAO:0000112 "The value of 'positive' in a classification scheme of \"positive or negative\"; the value of '20g' on the quantitative scale of mass." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "This term is currently a descendant of 'information content entity', which requires that it 'is about' something. A value specification of '20g' for a measurement data item of the mass of a particular mouse 'is about' the mass of that mouse. However there are cases where a value specification is not clearly about any particular. In the future we may change 'value specification' to remove the 'is about' requirement." xsd:string property_value: IAO:0000117 "PERSON:Bjoern Peters" xsd:string [Term] id: OBI:0002076 name: collection of specimens def: "A material entity that has two or more specimens as its parts." [] is_a: BFO:0000040 ! material entity intersection_of: BFO:0000040 ! material entity intersection_of: RO:0002351 OBI:0100051 {all_only="true"} ! has member specimen property_value: http://purl.org/dc/elements/1.1/source "Biobank" xsd:string property_value: IAO:0000111 "collection of specimens" xsd:string property_value: IAO:0000112 "Blood cells collected from multiple donors over the course of a study." xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "Details see tracker: https://sourceforge.net/p/obi/obi-terms/778/" xsd:string property_value: IAO:0000117 "Person: Chris Stoeckert, Jie Zheng" xsd:string property_value: IAO:0000119 "OBIB, OBI" xsd:string [Term] id: OBI:0002205 name: histologic grade according to AJCC 7th edition def: "A categorical value specification that is a histologic grade assigned to a tumor slide specimen according to the American Joint Committee on Cancer (AJCC) 7th Edition grading system." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "histologic grade according to AJCC 7th edition" xsd:string property_value: IAO:0000112 "G1:Well differentiated" xsd:string property_value: IAO:0000112 "G4: Undifferentiated" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002210 name: histologic grade according to the Fuhrman Nuclear Grading System def: "A categorical value specification that is a histologic grade assigned to a tumor slide specimen according to the Fuhrman Nuclear Grading System." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "histologic grade according to the Fuhrman Nuclear Grading System" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Histologic Grade (Fuhrman Nuclear Grading System)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002215 name: histologic grade for ovarian tumor def: "A categorical value specification that is a histologic grade assigned to a ovarian tumor." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "histologic grade for ovarian tumor" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002216 name: histologic grade for ovarian tumor according to a two-tier grading system def: "A histologic grade for ovarian tumor that is from a two-tier histological classification of tumors. " [] is_a: OBI:0002215 ! histologic grade for ovarian tumor property_value: IAO:0000111 "histologic grade for ovarian tumor according to a two-tier grading system" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002219 name: histologic grade for ovarian tumor according to the World Health Organization def: "A histologic grade for ovarian tumor that is from a histological classification by the World Health Organization (WHO)." [] is_a: OBI:0002215 ! histologic grade for ovarian tumor property_value: IAO:0000111 "histologic grade for ovarian tumor according to the World Health Organization" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002224 name: pathologic primary tumor stage for colon and rectum according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of colorectal cancer following the rules of the TNM American Joint Committee on Cancer (AJCC) version 7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic primary tumor stage for colon and rectum according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pT: Pathologic spread colorectal primary tumor (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002232 name: pathologic primary tumor stage for lung according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM American Joint Committee on Cancer (AJCC) version 7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic primary tumor stage for lung according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pT: Pathologic spread lung primary tumor (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002243 name: pathologic primary tumor stage for kidney according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic primary tumor stage for kidney according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pT: Pathologic spread kidney primary tumor (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002256 name: pathologic primary tumor stage for ovary according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic primary tumor stage for ovary according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pT: Pathologic spread ovarian primary tumor (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002270 name: pathologic lymph node stage for colon and rectum according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of colorectal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic lymph node stage for colon and rectum according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pN: Pathologic spread colon lymph nodes (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002279 name: pathologic lymph node stage for lung according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic lymph node stage for lung according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pN: Pathologic spread colon lymph nodes (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002284 name: pathologic lymph node stage for kidney according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic lymph node stage for kidney according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pN: Pathologic spread kidney lymph nodes (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002287 name: pathologic lymph node stage for ovary according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic lymph node stage for ovary according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "pN: Pathologic spread ovarian lymph nodes (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002290 name: pathologic distant metastases stage for colon according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of colon cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic distant metastases stage for colon according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "M: colon distant metastases (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002298 name: pathologic distant metastases stage for lung according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic distant metastases stage for lung according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "M: lung distant metastases (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002306 name: pathologic distant metastases stage for kidney according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic distant metastases stage for kidney according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "M: kidney distant Metastases (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002310 name: pathologic distant metastases stage for ovary according to AJCC 7th edition def: "A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "pathologic distant metastases stage for ovary according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "M: ovarian distant metastases (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002314 name: clinical tumor stage group according to AJCC 7th edition def: "A categorical value specification that is an assessment of the stage of a cancer according to the American Joint Committee on Cancer (AJCC) v7 staging systems." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "clinical tumor stage group according to AJCC 7th edition" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Clinical tumor stage group (AJCC 7th Edition)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002326 name: International Federation of Gynecology and Obstetrics cervical cancer stage value specification def: "A categorical value specification that is an assessment of the stage of a gynecologic cancer according to the International Federation of Gynecology and Obstetrics (FIGO) staging systems." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "International Federation of Gynecology and Obstetrics cervical cancer stage value specification" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Clinical FIGO stage" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002341 name: International Federation of Gynecology and Obstetrics ovarian cancer stage value specification def: "A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the FIGO classification system." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "International Federation of Gynecology and Obstetrics ovarian cancer stage value specification" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Pathologic Tumor Stage Grouping for ovarian cancer (FIGO)" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002356 name: performance status value specification def: "A categorical value specification that is an assessment of a participant's performance status (general well-being and activities of daily life)." [] is_a: OBI:0001930 ! categorical value specification property_value: IAO:0000111 "performance status value specification" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Performance Status Scale" xsd:string property_value: IAO:0000119 "https://en.wikipedia.org/wiki/Performance_status" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002357 name: Eastern Cooperative Oncology Group score value specification def: "A performance status value specification designed by the Eastern Cooperative Oncology Group to assess disease progression and its affect on the daily living abilities of the patient." [] is_a: OBI:0002356 ! performance status value specification property_value: IAO:0000111 "Eastern Cooperative Oncology Group score value specification" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "ECOG score" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002363 name: Karnofsky score vaue specification def: "A performance status value specification designed for classifying patients 16 years of age or older by their functional impairment." [] is_a: OBI:0002356 ! performance status value specification property_value: IAO:0000111 "Karnofsky score vaue specification" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "Chris Stoeckert, Helena Ellis" xsd:string property_value: IAO:0000118 "Karnofsky Score" xsd:string property_value: IAO:0000119 "NCI BBRB, OBI" xsd:string property_value: IAO:0000234 "NCI BBRB" xsd:string [Term] id: OBI:0002989 name: material supplier def: "A person or organization that provides material supplies to other people or organizations." [] is_a: BFO:0000040 ! material entity relationship: RO:0000087 OBI:0000018 ! has role material supplier role property_value: IAO:0000111 "material supplier" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "Rebecca Jackson" xsd:string property_value: IAO:0000233 "https://github.com/obi-ontology/obi/issues/1289" xsd:string [Term] id: OBI:0100026 name: organism def: "A material entity that is an individual living system, such as animal, plant, bacteria or virus, that is capable of replicating or reproducing, growth and maintenance in the right environment. An organism may be unicellular or made up, like humans, of many billions of cells divided into specialized tissues and organs." [] is_a: BFO:0000040 ! material entity union_of: NCBITaxon:10239 ! Viruses union_of: NCBITaxon:2 ! Bacteria union_of: NCBITaxon:2157 ! Archaea union_of: NCBITaxon:2759 ! Eukaryota property_value: IAO:0000111 "organism" xsd:string property_value: IAO:0000112 "animal" xsd:string property_value: IAO:0000112 "fungus" xsd:string property_value: IAO:0000112 "plant" xsd:string property_value: IAO:0000112 "virus" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "10/21/09: This is a placeholder term, that should ideally be imported from the NCBI taxonomy, but the high level hierarchy there does not suit our needs (includes plasmids and 'other organisms')" xsd:string property_value: IAO:0000116 "13-02-2009:\nOBI doesn't take position as to when an organism starts or ends being an organism - e.g. sperm, foetus.\nThis issue is outside the scope of OBI." xsd:string property_value: IAO:0000117 "GROUP: OBI Biomaterial Branch" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/Organism" xsd:string [Term] id: OBI:0100051 name: specimen def: "A material entity that has the specimen role." [] is_a: BFO:0000040 ! material entity intersection_of: BFO:0000040 ! material entity intersection_of: RO:0000087 OBI:0000112 ! has role specimen role relationship: RO:0000087 OBI:0000112 ! has role specimen role property_value: IAO:0000111 "specimen" xsd:string property_value: IAO:0000112 "Biobanking of blood taken and stored in a freezer for potential future investigations stores specimen." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "Note: definition is in specimen creation objective which is defined as an objective to obtain and store a material entity for potential use as an input during an investigation." xsd:string property_value: IAO:0000117 "PERSON: James Malone" xsd:string property_value: IAO:0000117 "PERSON: Philippe Rocca-Serra" xsd:string property_value: IAO:0000119 "GROUP: OBI Biomaterial Branch" xsd:string property_value: IAO:0000233 https://github.com/obi-ontology/obi/issues/1013 [Term] id: OBI:0200000 name: data transformation def: "A planned process that produces output data from input data." [] is_a: OBI:0000011 ! planned process relationship: OBI:0000293 IAO:0000027 {all_only="true"} ! has_specified_input data item relationship: OBI:0000417 OBI:0200166 ! achieves_planned_objective data transformation objective property_value: IAO:0000111 "data transformation" xsd:string property_value: IAO:0000112 "The application of a clustering protocol to microarray data or the application of a statistical testing method on a primary data set to determine a p-value." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "Helen Parkinson" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Melanie Courtot" xsd:string property_value: IAO:0000117 "Philippe Rocca-Serra" xsd:string property_value: IAO:0000117 "Richard Scheuermann" xsd:string property_value: IAO:0000117 "Ryan Brinkman" xsd:string property_value: IAO:0000117 "Tina Hernandez-Boussard" xsd:string property_value: IAO:0000118 "data analysis" xsd:string property_value: IAO:0000118 "data processing" xsd:string property_value: IAO:0000119 "Branch editors" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/obi.owl [Term] id: OBI:0200033 name: leave one out cross validation method def: "is a data transformation : leave-one-out cross-validation (LOOCV) involves using a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data" [] is_a: OBI:0000792 ! statistical model validation property_value: IAO:0000111 "leave one out cross validation method" xsd:string property_value: IAO:0000112 "The authors conducted leave-one-out cross validation to estimate the strength and accuracy of the differentially expressed filtered genes. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/3/368" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000116 "2009-11-10. Tracker: https://sourceforge.net/tracker/?func=detail&aid=2893049&group_id=177891&atid=886178" xsd:string property_value: IAO:0000117 "Person:Helen Parkinson" xsd:string [Term] id: OBI:0200041 name: k-means clustering def: "A k-means clustering is a data transformation which achieves a class discovery or partitioning objective, which takes as input a collection of objects (represented as points in multidimensional space) and which partitions them into a specified number k of clusters. The algorithm attempts to find the centers of natural clusters in the data. The most common form of the algorithm starts by partitioning the input points into k initial sets, either at random or using some heuristic data. It then calculates the mean point, or centroid, of each set. It constructs a new partition by associating each point with the closest centroid. Then the centroids are recalculated for the new clusters, and the algorithm repeated by alternate applications of these two steps until convergence, which is obtained when the points no longer switch clusters (or alternatively centroids are no longer changed)." [] is_a: APOLLO_SV:00000796 ! dataset creating is_a: OBI:0200171 ! partitioning data transformation is_a: OBI:0200175 ! class discovery data transformation relationship: OBI:0000299 OBI:0000648 ! has_specified_output clustered data set relationship: OBI:0000417 OBI:0200178 ! achieves_planned_objective class discovery objective property_value: IAO:0000111 "k-means clustering" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Philippe Rocca-Serra" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/K-means" xsd:string [Term] id: OBI:0200042 name: hierarchical clustering def: "A hierarchical clustering is a data transformation which achieves a class discovery objective, which takes as input data item and builds a hierarchy of clusters. The traditional representation of this hierarchy is a tree (visualized by a dendrogram), with the individual input objects at one end (leaves) and a single cluster containing every object at the other (root)." [] is_a: APOLLO_SV:00000796 ! dataset creating is_a: OBI:0200175 ! class discovery data transformation relationship: OBI:0000299 OBI:0000648 ! has_specified_output clustered data set relationship: OBI:0000417 OBI:0200178 ! achieves_planned_objective class discovery objective property_value: IAO:0000111 "hierarchical clustering" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/Data_clustering#Hierarchical_clustering" xsd:string [Term] id: OBI:0200050 name: dimensionality reduction def: "A dimensionality reduction is data partitioning which transforms each input m-dimensional vector (x_1, x_2, ..., x_m) into an output n-dimensional vector (y_1, y_2, ..., y_n), where n is smaller than m." [] is_a: APOLLO_SV:00000796 ! dataset creating is_a: OBI:0200175 ! class discovery data transformation relationship: OBI:0000299 OBI:0000648 ! has_specified_output clustered data set relationship: OBI:0000417 OBI:0200178 ! achieves_planned_objective class discovery objective property_value: IAO:0000111 "dimensionality reduction" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Melanie Courtot" xsd:string property_value: IAO:0000117 "Philippe Rocca-Serra" xsd:string property_value: IAO:0000118 "data projection" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string property_value: IAO:0000119 "PERSON: Melanie Courtot" xsd:string [Term] id: OBI:0200051 name: principal components analysis dimensionality reduction def: "A principal components analysis dimensionality reduction is a dimensionality reduction achieved by applying principal components analysis and by keeping low-order principal components and excluding higher-order ones." [] is_a: OBI:0200050 ! dimensionality reduction property_value: IAO:0000111 "principal components analysis dimensionality reduction" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Melanie Courtot" xsd:string property_value: IAO:0000117 "Philippe Rocca-Serra" xsd:string property_value: IAO:0000118 "pca data reduction" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string property_value: IAO:0000119 "PERSON: Melanie Courtot" xsd:string [Term] id: OBI:0200111 name: data visualization def: "An planned process that creates images, diagrams or animations from the input data." [] is_a: OBI:0000011 ! planned process relationship: OBI:0000293 IAO:0000027 ! has_specified_input data item relationship: OBI:0000293 IAO:0000027 {all_only="true"} ! has_specified_input data item property_value: IAO:0000111 "data visualization" xsd:string property_value: IAO:0000112 "Generation of a heatmap from a microarray dataset" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000117 "Melanie Courtot" xsd:string property_value: IAO:0000117 "Tina Boussard" xsd:string property_value: IAO:0000118 "data encoding as image" xsd:string property_value: IAO:0000118 "visualization" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string property_value: IAO:0000119 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000119 "PERSON: Tina Boussard" xsd:string property_value: IAO:0000232 "Possible future hierarchy might include this:\ninformation_encoding\n>data_encoding\n>>image_encoding" xsd:string [Term] id: OBI:0200166 name: data transformation objective def: "An objective specification to transformation input data into output data" [] is_a: IAO:0000005 ! objective specification property_value: IAO:0000111 "data transformation objective" xsd:string property_value: IAO:0000112 "normalize objective" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "Modified definition in 2013 Philly OBI workshop" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0200171 name: partitioning data transformation def: "A partitioning data transformation is a data transformation that has objective partitioning." [] is_a: OBI:0200000 ! data transformation relationship: OBI:0000417 OBI:0200172 ! achieves_planned_objective partitioning objective property_value: IAO:0000111 "partitioning data transformation" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0200172 name: partitioning objective def: "A partitioning objective is a data transformation objective where the aim is to generate a collection of disjoint non-empty subsets whose union equals a non-empty input set." [] is_a: OBI:0200166 ! data transformation objective property_value: IAO:0000111 "partitioning objective" xsd:string property_value: IAO:0000112 "A k-means clustering which has partitioning objective is a data transformation in which the input data is partitioned into k output sets." xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Elisabetta Manduchi" xsd:string property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string [Term] id: OBI:0200175 name: class discovery data transformation def: "A class discovery data transformation (sometimes called unsupervised classification) is a data transformation that has objective class discovery." [] is_a: OBI:0200000 ! data transformation property_value: IAO:0000111 "class discovery data transformation" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "clustering data transformation" xsd:string property_value: IAO:0000118 "unsupervised classification data transformation" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0200178 name: class discovery objective def: "A class discovery objective (sometimes called unsupervised classification) is a data transformation objective where the aim is to organize input data (typically vectors of attributes) into classes, where the number of classes and their specifications are not known a priori. Depending on usage, the class assignment can be definite or probabilistic." [] is_a: OBI:0200166 ! data transformation objective property_value: IAO:0000111 "class discovery objective" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "clustering objective" xsd:string property_value: IAO:0000118 "discriminant analysis objective" xsd:string property_value: IAO:0000118 "unsupervised classification objective" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0200179 name: class prediction objective def: "A class prediction objective (sometimes called supervised classification) is a data transformation objective where the aim is to create a predictor from training data through a machine learning technique. The training data consist of pairs of objects (typically vectors of attributes) and\nclass labels for these objects. The resulting predictor can be used to attach class labels to any valid novel input object. Depending on usage, the prediction can be definite or probabilistic. A classification is learned from the training data and can then be tested on test data." [] is_a: OBI:0200166 ! data transformation objective property_value: IAO:0000111 "class prediction objective" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "classification objective" xsd:string property_value: IAO:0000118 "supervised classification objective" xsd:string property_value: IAO:0000119 "PERSON: Elisabetta Manduchi" xsd:string property_value: IAO:0000119 "PERSON: James Malone" xsd:string [Term] id: OBI:0200188 name: cross validation objective def: "A cross validation objective is a data transformation objective in which the aim is to partition a sample of data into subsets such that the analysis is initially performed on a single subset, while the other subset(s) are retained for subsequent use in confirming and validating the initial analysis." [] is_a: OBI:0200172 ! partitioning objective property_value: IAO:0000111 "cross validation objective" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 "James Malone" xsd:string property_value: IAO:0000118 "rotation estimation objective" xsd:string property_value: IAO:0000119 "WEB: http://en.wikipedia.org/wiki/Cross_validation" xsd:string [Term] id: OBI:0200190 name: clustered data visualization def: "A data visualization which has input of a clustered data set and produces an output of a report graph which is capable of rendering data of this type." [] is_a: OBI:0200111 ! data visualization relationship: OBI:0000293 OBI:0000648 {all_only="true"} ! has_specified_input clustered data set property_value: IAO:0000111 "clustered data visualization" xsd:string property_value: IAO:0000114 IAO:0000123 property_value: IAO:0000117 "James Malone" xsd:string [Term] id: ObsoleteClass name: Obsolete Class [Term] id: PATO:0000001 name: quality def: "A dependent entity that inheres in a bearer by virtue of how the bearer is related to other entities" [PATOC:GVG] {type="owl:Axiom"} is_a: BFO:0000020 ! specifically dependent continuant [Term] id: SWO:0000260 name: publisher role def: "A publisher role is a role borne by an organization or individual in which they are responsible for making software available to a particular consumer group. Such organizations or individuals do need to be involved in the development of the software." [] is_a: BFO:0000023 ! role property_value: IAO:0000119 "James Malone" xsd:string [Term] id: SWO:0000392 name: software developer role def: "Software developer role is a role borne by an organization or individual in which they are responsible for authoring software." [] is_a: BFO:0000023 ! role property_value: IAO:0000119 "James Malone" xsd:string [Term] id: SWO:0000396 name: software developer organization def: "An organization or legal entity (including single person) that is responsible for developing software. Developing includes aspects of design, coding and testing." [] is_a: OBI:0000245 ! organization relationship: RO:0000087 SWO:0000392 ! has role software developer role [Term] id: SWO:0000397 name: software publisher organization def: "An organization or legal entity (including single person) that is responsible for publishing software. Publishing here includes tasks such as designing and producing physical products, technical customer support, licensing arrangements and marketing." [] is_a: OBI:0000245 ! organization relationship: RO:0000087 SWO:0000260 ! has role publisher role [Term] id: UO:0000001 name: length unit def: "A unit which is a standard measure of the distance between two points." [] is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "length unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000002 name: mass unit def: "A unit which is a standard measure of the amount of matter/energy of a physical object." [] is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "mass unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000003 name: time unit def: "A unit which is a standard measure of the dimension in which events occur in sequence." [] is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "time unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000005 name: temperature unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "temperature unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000006 name: substance unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "substance unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000051 name: concentration unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "concentration unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000095 name: volume unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "volume unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000105 name: frequency unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "frequency unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000270 name: volumetric flow rate unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "volumetric flow rate unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: UO:0000280 name: rate unit is_a: IAO:0000003 ! measurement unit label property_value: IAO:0000111 "rate unit" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/uo.owl [Term] id: https://w3id.org/aio/AbstractRNNCell name: AbstractRNNCell def: "Abstract object representing an RNN cell. This is the base class for implementing RNN cells with custom behavior." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AbstractRNNCell] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ActivationLayer name: Activation Layer def: "Applies an activation function to an output." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ActiveLearning name: Active Learning def: "Methods which can interactively query a user (or some other information source) to label new data points with the desired outputs." [https://en.wikipedia.org/wiki/Active_learning_(machine_learning)] {type="owl:Axiom"} synonym: "Query Learning" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/ActivityBias name: Activity Bias def: "A type of selection bias that occurs when systems/platforms get their training data from their most active users, rather than those less active (or inactive)." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/ActivityRegularizationLayer name: ActivityRegularization Layer def: "Layer that applies an update to the cost function based input activity." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ActivityRegularization] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/AdaptiveAvgPool1DLayer name: AdaptiveAvgPool1D Layer def: "Applies a 1D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveAvgPool1D" EXACT [] synonym: "AdaptiveAvgPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AdaptiveAvgPool2DLayer name: AdaptiveAvgPool2D Layer def: "Applies a 2D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveAvgPool2D" EXACT [] synonym: "AdaptiveAvgPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AdaptiveAvgPool3DLayer name: AdaptiveAvgPool3D Layer def: "Applies a 3D adaptive average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveAvgPool3D" EXACT [] synonym: "AdaptiveAvgPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AdaptiveMaxPool1DLayer name: AdaptiveMaxPool1D Layer def: "Applies a 1D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveMaxPool1D" EXACT [] synonym: "AdaptiveMaxPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AdaptiveMaxPool2DLayer name: AdaptiveMaxPool2D Layer def: "Applies a 2D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveMaxPool2D" EXACT [] synonym: "AdaptiveMaxPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AdaptiveMaxPool3DLayer name: AdaptiveMaxPool3D Layer def: "Applies a 3D adaptive max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AdaptiveMaxPool3D" EXACT [] synonym: "AdaptiveMaxPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AddLayer name: Add Layer def: "Layer that adds a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Add] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/AdditiveAttentionLayer name: AdditiveAttention Layer def: "Additive attention layer, a.k.a. Bahdanau-style attention." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AdditiveAttention] {type="owl:Axiom"} is_a: https://w3id.org/aio/AttentionLayer ! Attention Layer [Term] id: https://w3id.org/aio/Adversarial-ResistantLLM name: Adversarial-Resistant LLM def: "An adversarial-resistant LLM is engineered to withstand or mitigate the effects of adversarial attacks, ensuring reliable performance even in the presence of deliberately misleading input designed to confuse the model." [TBD] {type="owl:Axiom"} synonym: "adversarial attacks" RELATED [] synonym: "Robust LLM" EXACT [] synonym: "robustness" RELATED [] is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/AlphaDropoutLayer name: AlphaDropout Layer def: "Applies Alpha Dropout to the input. Alpha Dropout is a Dropout that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AlphaDropout] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/AmplificationBias name: Amplification Bias def: "Arises when the distribution over prediction outputs is skewed in comparison to the prior distribution of the prediction target." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/ProcessingBias ! Processing Bias [Term] id: https://w3id.org/aio/AnchoringBias name: Anchoring Bias def: "A cognitive bias, the influence of a particular reference point or anchor on people’s decisions. Often more fully referred to as anchoring-and-adjustment, or anchoring-and-adjusting: after an anchor is set, people adjust insufficiently from that anchor point to arrive at a final answer. Decision makers are biased towards an initially presented value." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/AnnotatorReportingBias name: Annotator Reporting Bias def: "When users rely on automation as a heuristic replacement for their own information seeking and processing. A form of individual bias but often discussed as a group bias, or the larger effects on natural language processing models." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ApplicationFocus name: Application Focus def: "An abstract parent class grouping LLMs based on model application focus." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/LargeLanguageModel ! Large Language Model [Term] id: https://w3id.org/aio/ArtificialNeuralNetwork name: Artificial Neural Network def: "An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives a signal then processes it and can signal neurons connected to it. The \"signal\" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as Learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times." [https://en.wikipedia.org/wiki/Artificial_neural_network] {type="owl:Axiom"} synonym: "ANN" EXACT [] synonym: "NN" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/AssociationRuleLearning name: Association Rule Learning def: "A rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness." [https://en.wikipedia.org/wiki/Association_rule_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/SupervisedLearning ! Supervised Learning [Term] id: https://w3id.org/aio/AttentionLayer name: Attention Layer def: "Dot-product attention layer, a.k.a. Luong-style attention." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Attention] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/AutoEncoderNetwork name: Auto Encoder Network def: "An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised Learning). The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”). (https://en.wikipedia.org/wiki/Autoencoder)" [https://en.wikipedia.org/wiki/Autoencoder] {type="owl:Axiom"} comment: Input, Hidden, Matched Output-Input synonym: "AE" EXACT [] is_a: https://w3id.org/aio/UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network [Term] id: https://w3id.org/aio/AutomationComplacencyBias name: Automation Complacency Bias def: "When humans over-rely on automated systems or have their skills attenuated by such over-reliance (e.g., spelling and autocorrect or spellcheckers)." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Automation Complaceny" EXACT [] is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/AutoregressiveLanguageModel name: Autoregressive Language Model def: "An autoregressive language model is a type of language model that generates text sequentially, predicting one token at a time based on the previously generated tokens. It excels at natural language generation tasks by modeling the probability distribution over sequences of tokens." [TBD] {type="owl:Axiom"} synonym: "Autoregressive Language Model" EXACT [] synonym: "generative language model" RELATED [] synonym: "sequence-to-sequence model" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/AvailabilityHeuristicBias name: Availability Heuristic Bias def: "A mental shortcut whereby people tend to overweight what comes easily or quickly to mind, meaning that what is easier to recall—e.g., more “available”—receives greater emphasis in judgement and decision-making." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Availability Bias" EXACT [] synonym: "Availability Heuristic" EXACT [] is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/AverageLayer name: Average Layer def: "Layer that averages a list of inputs element-wise. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Average] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/AveragePooling1DLayer name: AveragePooling1D Layer def: "Average pooling for temporal data. Downsamples the input representation by taking the average value over the window defined by pool_size. The window is shifted by strides. The resulting output when using \"valid\" padding option has a shape of: output_shape = (input_shape - pool_size + 1) / strides). The resulting output shape when using the \"same\" padding option is: output_shape = input_shape / strides." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling1D] {type="owl:Axiom"} synonym: "AvgPool1D" EXACT [] synonym: "AvgPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AveragePooling2DLayer name: AveragePooling2D Layer def: "Average pooling operation for spatial data. Downsamples the input along its spatial dimensions (height and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension. The resulting output when using \"valid\" padding option has a shape (number of rows or columns) of: output_shape = math.floor((input_shape - pool_size) / strides) + 1 (when input_shape >= pool_size). The resulting output shape when using the \"same\" padding option is: output_shape = math.floor((input_shape - 1) / strides) + 1." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D] {type="owl:Axiom"} synonym: "AvgPool2D" EXACT [] synonym: "AvgPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AveragePooling3DLayer name: AveragePooling3D Layer def: "Average pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the average value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling3D] {type="owl:Axiom"} synonym: "AvgPool3D" EXACT [] synonym: "AvgPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AvgPool1DLayer name: AvgPool1D Layer def: "Applies a 1D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AvgPool1D" EXACT [] synonym: "AvgPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AvgPool2DLayer name: AvgPool2D Layer def: "Applies a 2D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AvgPool2D" EXACT [] synonym: "AvgPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/AvgPool3DLayer name: AvgPool3D Layer def: "Applies a 3D average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "AvgPool3D" EXACT [] synonym: "AvgPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/BatchNorm1DLayer name: BatchNorm1D Layer def: "Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "BatchNorm1D" EXACT [] synonym: "BatchNorm1d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/BatchNorm2DLayer name: BatchNorm2D Layer def: "Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "BatchNorm2D" EXACT [] synonym: "BatchNorm2d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/BatchNorm3DLayer name: BatchNorm3D Layer def: "Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "BatchNorm3D" EXACT [] synonym: "BatchNorm3d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/BatchNormalizationLayer name: BatchNormalization Layer def: "Layer that normalizes its inputs. Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference. During training (i.e. when using fit() or when calling the layer/model with the argument training=True), the layer normalizes its output using the mean and standard deviation of the current batch of inputs. That is to say, for each channel being normalized, the layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, where: epsilon is small constant (configurable as part of the constructor arguments), gamma is a learned scaling factor (initialized as 1), which can be disabled by passing scale=False to the constructor. beta is a learned offset factor (initialized as 0), which can be disabled by passing center=False to the constructor. During inference (i.e. when using evaluate() or predict() or when calling the layer/model with the argument training=False (which is the default), the layer normalizes its output using a moving average of the mean and standard deviation of the batches it has seen during training. That is to say, it returns gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta. self.moving_mean and self.moving_var are non-trainable variables that are updated each time the layer in called in training mode, as such: moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization] {type="owl:Axiom"} is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/BayesianNetwork name: Bayesian Network def: "A probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG)." [https://en.wikipedia.org/wiki/Bayesian_network] {type="owl:Axiom"} is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/BehavioralBias name: Behavioral Bias def: "Systematic distortions in user behavior across platforms or contexts, or across users represented in different datasets." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/Bias name: Bias def: "Systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others." [https://www.merriam-webster.com/dictionary/bias] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/Biclustering name: Biclustering def: "Methods that simultaneously cluster the rows and columns of a matrix." [https://en.wikipedia.org/wiki/Biclustering] {type="owl:Axiom"} synonym: "Block Clustering" EXACT [] synonym: "Co-clustering" EXACT [] synonym: "Joint Clustering" EXACT [] synonym: "Two-mode Clustering" EXACT [] synonym: "Two-way Clustering" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/BidirectionalLayer name: Bidirectional Layer def: "Bidirectional wrapper for RNNs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/BinaryClassification name: Binary Classification def: "Methods that classify the elements of a set into two groups (each called class) on the basis of a classification rule." [https://en.wikipedia.org/wiki/Binary_classification] {type="owl:Axiom"} is_a: https://w3id.org/aio/Classification ! Classification [Term] id: https://w3id.org/aio/BoltzmannMachineNetwork name: Boltzmann Machine Network def: "A Boltzmann machine is a type of stochastic recurrent neural network. It is a Markov random field. It was translated from statistical physics for use in cognitive science. The Boltzmann machine is based on a stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model that is a stochastic Ising Model[2] and applied to machine Learning." [https://en.wikipedia.org/wiki/Boltzmann_machine] {type="owl:Axiom"} comment: Backfed Input, Probabilistic Hidden synonym: "BM" EXACT [] synonym: "Sherrington–Kirkpatrick model with external field" EXACT [] synonym: "stochastic Hopfield network with hidden units" EXACT [] synonym: "stochastic Ising-Lenz-Little model" EXACT [] is_a: https://w3id.org/aio/SymmetricallyConnectedNetwork ! Symmetrically Connected Network [Term] id: https://w3id.org/aio/CategoricalFeaturesPreprocessingLayer name: Categorical Features Preprocessing Layer def: "A layer that performs categorical data preprocessing operations." [https://keras.io/guides/preprocessing_layers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/CategoryEncodingLayer name: CategoryEncoding Layer def: "A preprocessing layer which encodes integer features. This layer provides options for condensing data into a categorical encoding when the total number of tokens are known in advance. It accepts integer values as inputs, and it outputs a dense or sparse representation of those inputs. For integer inputs where the total number of tokens is not known, use tf.keras.layers.IntegerLookup instead." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding] {type="owl:Axiom"} is_a: https://w3id.org/aio/CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer [Term] id: https://w3id.org/aio/CausalGraphicalModel name: Causal Graphical Model def: "Probabilistic graphical models used to encode assumptions about the data-generating process." [https://en.wikipedia.org/wiki/Causal_graph] {type="owl:Axiom"} synonym: "Casaul Bayesian Network" EXACT [] synonym: "Casaul Graph" EXACT [] synonym: "DAG" EXACT [] synonym: "Directed Acyclic Graph" EXACT [] synonym: "Path Diagram" EXACT [] is_a: https://w3id.org/aio/ProbabilisticGraphicalModel ! Probabilistic Graphical Model [Term] id: https://w3id.org/aio/CausalLLM name: Causal LLM def: "A causal LLM only attends to previous tokens in the sequence when generating text, modeling the probability distribution autoregressively from left-to-right or causally." [TBD] {type="owl:Axiom"} synonym: "autoregressive" RELATED [] synonym: "Causal LLM" EXACT [] synonym: "unidirectional" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/CenterCropLayer name: CenterCrop Layer def: "A preprocessing layer which crops images. This layers crops the central portion of the images to a target size. If an image is smaller than the target size, it will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/CenterCrop] {type="owl:Axiom"} is_a: https://w3id.org/aio/ImagePreprocessingLayer ! Image Preprocessing Layer [Term] id: https://w3id.org/aio/Classification name: Classification def: "Methods that distinguishand distribute kinds of \"things\" into different groups." [https://en.wikipedia.org/wiki/Classification_(general_theory)] {type="owl:Axiom"} is_a: https://w3id.org/aio/SupervisedLearning ! Supervised Learning [Term] id: https://w3id.org/aio/CleaningAndNormalization name: Cleaning And Normalization def: "Removing irrelevant data, correcting typos, and standardizing text to reduce noise and ensure consistency in the data." [TBD] {type="owl:Axiom"} synonym: "Data cleaning" RELATED [] synonym: "Data Cleansing" EXACT [] synonym: "Standardization" EXACT [] synonym: "Text normalization" RELATED [] is_a: https://w3id.org/aio/DataPreparation ! Data Preparation [Term] id: https://w3id.org/aio/Clustering name: Clustering def: "Methods that group a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters)." [https://en.wikipedia.org/wiki/Cluster_analysis] {type="owl:Axiom"} synonym: "Cluster analysis" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/CognitiveBias name: Cognitive Bias def: "A broad term referring generally to a systematic pattern of deviation from rational judgement and decision-making. A large variety of cognitive biases have been identified over many decades of research in judgement and decision-making, some of which are adaptive mental shortcuts known as heuristics." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/CompositionalGeneralizationLLM name: Compositional Generalization LLM def: "A compositional generalization LLM is trained to understand and recombine the underlying compositional structures in language, enabling better generalization to novel combinations and out-of-distribution examples." [TBD] {type="owl:Axiom"} synonym: "Compositional Generalization LLM" EXACT [] synonym: "out-of-distribution generalization" RELATED [] synonym: "systematic generalization" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/ComputationalBias name: Computational Bias def: "A systematic tendency which causes differences between results and facts. The bias exists in numbers of the process of data analysis, including the source of the data, the estimator chosen, and the ways the data was analyzed." [https://en.wikipedia.org/wiki/Bias_(statistics)] {type="owl:Axiom"} synonym: "Statistical Bias" EXACT [] is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/ConcatenateLayer name: Concatenate Layer def: "Layer that concatenates a list of inputs. It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor that is the concatenation of all inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Concatenate] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/ConceptDriftBias name: Concept Drift Bias def: "Use of a system outside the planned domain of application, and a common cause of performance gaps between laboratory settings and the real world." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Concept Drift" EXACT [] is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/ConfirmationBias name: Confirmation Bias def: "A cognitive bias where people tend to prefer information that aligns with, or confirms, their existing beliefs. People can exhibit confirmation bias in the search for, interpretation of, and recall of information. In the famous Wason selection task experiments, participants repeatedly showed a preference for confirmation over falsification. They were tasked with identifying an underlying rule that applied to number triples they were shown, and they overwhelmingly tested triples that confirmed rather than falsified their hypothesized rule." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ConsumerBias name: Consumer Bias def: "Arises when an algorithm or platform provides users with a new venue within which to express their biases, and may occur from either side, or party, in a digital interaction.." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ContentProductionBias name: Content Production Bias def: "Arises from structural, lexical, semantic, and syntactic differences in the contents generated by users." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/ContinualLearning name: Continual Learning def: "A concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available any more during training new ones." [https://paperswithcode.com/task/continual-learning] {type="owl:Axiom"} synonym: "Incremental Learning" EXACT [] synonym: "Life-Long Learning" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/ContinualLearningLLM name: Continual Learning LLM def: "A continual learning LLM is designed to continually acquire new knowledge and skills over time, without forgetting previously learned information. This allows the model to adapt and expand its capabilities as new data becomes available." [TBD] {type="owl:Axiom"} synonym: "catastrophic forgetting" RELATED [] synonym: "CL-LLM" EXACT [] synonym: "Continual Learning LLM" EXACT [] synonym: "lifelong learning" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/ContrastiveLearning name: Contrastive Learning def: "Learning that encourages augmentations (views) of the same input to have more similar representations compared to augmentations of different inputs." [https://arxiv.org/abs/2202.14037] {type="owl:Axiom"} is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/ContrastiveLearningLLM name: Contrastive Learning LLM def: "A contrastive learning LLM is trained to pull semantically similar samples closer together and push dissimilar samples apart in the representation space, learning high-quality features useful for downstream tasks." [TBD] {type="owl:Axiom"} synonym: "Contrastive Learning LLM" EXACT [] synonym: "Representation learning" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/ControllableLLM name: Controllable LLM def: "A controllable LLM allows for explicit control over certain attributes of the generated text, such as style, tone, topic, or other desired characteristics, through conditioning or specialized training objectives." [TBD] {type="owl:Axiom"} synonym: "conditional generation" RELATED [] synonym: "Controllable LLM" EXACT [] synonym: "guided generation" RELATED [] is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/ConvLSTM1DLayer name: ConvLSTM1D Layer def: "1D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/ConvLSTM2DLayer name: ConvLSTM2D Layer def: "2D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/ConvLSTM3DLayer name: ConvLSTM3D Layer def: "3D Convolutional LSTM. Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM3D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/Convolution1DLayer name: Convolution1D Layer def: "1D convolution layer (e.g. temporal convolution)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D] {type="owl:Axiom"} synonym: "Conv1d" EXACT [] synonym: "Conv1D Layer" EXACT [] synonym: "Convolution1D" EXACT [] synonym: "Convolution1d" EXACT [] synonym: "nn.Conv1d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Convolution1DTransposeLayer name: Convolution1DTranspose Layer def: "Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1DTranspose] {type="owl:Axiom"} synonym: "Conv1DTranspose Layer" EXACT [] synonym: "Convolution1DTranspose" EXACT [] synonym: "Convolution1dTranspose" EXACT [] synonym: "ConvTranspose1d" EXACT [] synonym: "nn.ConvTranspose1d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Convolution2DLayer name: Convolution2D Layer def: "2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format=\"channels_last\". You can use None when a dimension has variable size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D] {type="owl:Axiom"} synonym: "Conv2d" EXACT [] synonym: "Conv2D Layer" EXACT [] synonym: "Convolution2D" EXACT [] synonym: "Convolution2d" EXACT [] synonym: "nn.Conv2d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Convolution2DTransposeLayer name: Convolution2DTranspose Layer def: "Transposed convolution layer (sometimes called Deconvolution)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose] {type="owl:Axiom"} synonym: "Conv2DTranspose Layer" EXACT [] synonym: "Convolution2DTranspose" EXACT [] synonym: "Convolution2dTranspose" EXACT [] synonym: "ConvTranspose2d" EXACT [] synonym: "nn.ConvTranspose2d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Convolution3DLayer name: Convolution3D Layer def: "3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 1) for 128x128x128 volumes with a single channel, in data_format=\"channels_last\"." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3D] {type="owl:Axiom"} synonym: "Conv3d" EXACT [] synonym: "Conv3D Layer" EXACT [] synonym: "Convolution3D" EXACT [] synonym: "Convolution3d" EXACT [] synonym: "nn.Conv3d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Convolution3DTransposeLayer name: Convolution3DTranspose Layer def: "Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 3) for a 128x128x128 volume with 3 channels if data_format=\"channels_last\"." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3DTranspose] {type="owl:Axiom"} synonym: "Conv3DTranspose Layer" EXACT [] synonym: "Convolution3DTranspose" EXACT [] synonym: "Convolution3dTranspose" EXACT [] synonym: "ConvTranspose3d" EXACT [] synonym: "nn.ConvTranspose3d" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ConvolutionalLayer name: Convolutional Layer def: "A convolutional layer is the main building block of a CNN. It contains a set of filters (or kernels), parameters of which are to be learned throughout the training. The size of the filters is usually smaller than the actual image. Each filter convolves with the image and creates an activation map." [https://www.sciencedirect.com/topics/engineering/convolutional-layer#\:~\:text=A%20convolutional%20layer%20is%20the\,and%20creates%20an%20activation%20map.] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Cropping1DLayer name: Cropping1D Layer def: "Cropping layer for 1D input (e.g. temporal sequence). It crops along the time dimension (axis 1)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/Cropping2DLayer name: Cropping2D Layer def: "Cropping layer for 2D input (e.g. picture). It crops along spatial dimensions, i.e. height and width." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Cropping3DLayer name: Cropping3D Layer def: "Cropping layer for 3D data (e.g. spatial or spatio-temporal)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping3D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Cross-DomainLLM name: Cross-Domain LLM def: "A cross-domain LLM is capable of performing well across a wide range of domains without significant loss in performance, facilitated by advanced domain adaptation techniques." [TBD] {type="owl:Axiom"} synonym: "cross-domain transfer" RELATED [] synonym: "domain adaptation" RELATED [] synonym: "Domain-General LLM" EXACT [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/CurriculumLearning name: Curriculum Learning def: "Training the model on simpler tasks or easier data first, then gradually introducing more complex tasks to improve learning efficiency and performance." [TBD] {type="owl:Axiom"} synonym: "Complexity grading" RELATED [] synonym: "Sequential Learning" EXACT [] synonym: "Sequential learning" RELATED [] synonym: "Structured Learning" EXACT [] is_a: https://w3id.org/aio/TrainingStrategies ! Training Strategies [Term] id: https://w3id.org/aio/CurriculumLearningLLM name: Curriculum Learning LLM def: "A curriculum learning LLM is trained by presenting learning examples in a meaningful order from simple to complex, mimicking the learning trajectory followed by humans." [TBD] {type="owl:Axiom"} synonym: "Curriculum Learning LLM" EXACT [] synonym: "Learning progression" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/Data-to-TextLLM name: Data-to-Text LLM def: "A data-to-text LLM generates natural language descriptions from structured data sources like tables, graphs, knowledge bases, etc. Requiring grounding meaning representations." [TBD] {type="owl:Axiom"} synonym: "Data-to-Text LLM" EXACT [] synonym: "Meaning representation" EXACT [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/DataAugmentation name: Data Augmentation def: "Expanding the training dataset artificially by modifying existing data points to improve the model's robustness and generalization ability." [TBD] {type="owl:Axiom"} synonym: "Data Enrichment" EXACT [] synonym: "Data Expansion" EXACT [] synonym: "Paraphrasing" RELATED [] synonym: "Synonym replacement" RELATED [] is_a: https://w3id.org/aio/DataEnhancement ! DataEnhancement [Term] id: https://w3id.org/aio/DataDredgingBias name: Data Dredging Bias def: "A statistical bias in which testing huge numbers of hypotheses of a dataset may appear to yield statistical significance even when the results are statistically nonsignificant." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Data Dredging" EXACT [] is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/DataEnhancement name: DataEnhancement def: "The processes and techniques used to improve data quality and value for better decision-making, analysis, and AI model training." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/Preprocessing ! Preprocessing [Term] id: https://w3id.org/aio/DataGenerationBias name: Data Generation Bias def: "Arises from the addition of synthetic or redundant data samples to a dataset." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/DataImputation name: Data Imputation def: "Methods that replace missing data with substituted values." [https://en.wikipedia.org/wiki/Imputation_(statistics)] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/DataPreparation name: Data Preparation def: "Techniques focused on preparing raw data for training, including cleaning, normalization, and tokenization." [TBD] {type="owl:Axiom"} synonym: "Data Assembly" EXACT [] synonym: "Data Curation" EXACT [] synonym: "Data Processing" EXACT [] is_a: https://w3id.org/aio/Preprocessing ! Preprocessing [Term] id: https://w3id.org/aio/DecisionTree name: Decision Tree def: "A decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility." [https://en.wikipedia.org/wiki/Decision_tree] {type="owl:Axiom"} is_a: https://w3id.org/aio/Classification ! Classification [Term] id: https://w3id.org/aio/DecoderLLM name: Decoder LLM def: "In the decoder-only architecture, the model consists of only a decoder, which is trained to predict the next token in a sequence given the previous tokens. The critical difference between the Decoder-only architecture and the Encoder-Decoder architecture is that the Decoder-only architecture does not have an explicit encoder to summarize the input information. Instead, the information is encoded implicitly in the hidden state of the decoder, which is updated at each step of the generation process." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder%2Donly&text=These%20models%20have%20a%20pre\,Named%20entity%20recognition] {type="owl:Axiom"} synonym: "LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/DeconvolutionalNetwork name: Deconvolutional Network def: "Deconvolutional Networks, a framework that permits the unsupervised construction of hierarchical image representations. These representations can be used for both low-level tasks such as denoising, as well as providing features for object recognition. Each level of the hierarchy groups information from the level beneath to form more complex features that exist over a larger scale in the image. (https://ieeexplore.ieee.org/document/5539957)" [https://ieeexplore.ieee.org/document/5539957] {type="owl:Axiom"} comment: Input, Kernel, Convolutional/Pool, Output synonym: "DN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/DeepActiveLearning name: Deep Active Learning def: "The combination of deep learning and active learning, where active learning attempts to maximize a model’s performance gain while annotating the fewest samples possible." [https://arxiv.org/pdf/2009.00236.pdf] {type="owl:Axiom"} synonym: "DeepAL" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/DeepBeliefNetwork name: Deep Belief Network def: "In machine Learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables (\"hidden units\"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this Learning step, a DBN can be further trained with supervision to perform classification. DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a \"visible\" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the \"lowest\" pair of layers (the lowest visible layer is a training set). The observation that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep Learning algorithms. (https://en.wikipedia.org/wiki/Deep_belief_network)" [https://en.wikipedia.org/wiki/Deep_belief_network] {type="owl:Axiom"} comment: Backfed Input, Probabilistic Hidden, Hidden, Matched Output-Input synonym: "DBN" EXACT [] is_a: https://w3id.org/aio/UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network [Term] id: https://w3id.org/aio/DeepConvolutionalInverseGraphicsNetwork name: Deep Convolutional Inverse Graphics Network def: "A Deep Convolution Inverse Graphics Network (DC-IGN) is a model that learns an interpretable representation of images. This representation is disentangled with respect to transformations such as out-of-plane rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm. (https://arxiv.org/abs/1503.03167)" [TBD] {type="owl:Axiom"} comment: Input, Kernel, Convolutional/Pool, Probabilistic Hidden, Convolutional/Pool, Kernel, Output synonym: "DCIGN" EXACT [] is_a: https://w3id.org/aio/AutoEncoderNetwork ! Auto Encoder Network [Term] id: https://w3id.org/aio/DeepConvolutionalNetwork name: Deep Convolutional Network def: "A convolutional neural network (CNN, or ConvNet) is a class of artificial neural network, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. CNNs are regularized versions of multilayer perceptrons. (https://en.wikipedia.org/wiki/Convolutional_neural_network)" [https://en.wikipedia.org/wiki/Convolutional_neural_network] {type="owl:Axiom"} comment: Input, Kernel, Convolutional/Pool, Hidden, Output synonym: "CNN" EXACT [] synonym: "ConvNet" EXACT [] synonym: "Convolutional Neural Network" EXACT [] synonym: "DCN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/DeepFeedForward name: Deep FeedForward def: "The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network." [https://en.wikipedia.org/wiki/Feedforward_neural_network] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "DFF" EXACT [] synonym: "Feedforward Network" EXACT [] synonym: "FFN" EXACT [] synonym: "MLP" EXACT [] synonym: "Multilayer Perceptoron" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/DeepNeuralNetwork name: Deep Neural Network def: "A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers.[13][2] There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions. (https://en.wikipedia.org/wiki/Deep_Learning#:~:text=A%20deep%20neural%20network%20(DNN,weights%2C%20biases%2C%20and%20functions.)" [TBD] {type="owl:Axiom"} synonym: "DNN" EXACT [] is_a: https://w3id.org/aio/ArtificialNeuralNetwork ! Artificial Neural Network [Term] id: https://w3id.org/aio/DeepTransferLearning name: Deep Transfer Learning def: "Deep transfer learning methods relax the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data." [https://arxiv.org/abs/1808.01974] {type="owl:Axiom"} is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/DenoisingAutoEncoder name: Denoising Auto Encoder def: "Denoising Auto Encoders (DAEs) take a partially corrupted input and are trained to recover the original undistorted input. In practice, the objective of denoising autoencoders is that of cleaning the corrupted input, or denoising. (https://en.wikipedia.org/wiki/Autoencoder)" [https://doi.org/10.1145/1390156.1390294] {type="owl:Axiom"} comment: Noisy Input, Hidden, Matched Output-Input synonym: "DAE" EXACT [] synonym: "Denoising Autoencoder" EXACT [] is_a: https://w3id.org/aio/AutoEncoderNetwork ! Auto Encoder Network [Term] id: https://w3id.org/aio/DenseFeaturesLayer name: DenseFeatures Layer def: "A layer that produces a dense Tensor based on given feature_columns. Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor. This layer can be called multiple times with different features. This is the V2 version of this layer that uses name_scopes to create variables instead of variable_scopes. But this approach currently lacks support for partitioned variables. In that case, use the V1 version instead." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DenseFeatures] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/DenseLayer name: Dense Layer def: "Just your regular densely-connected NN layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/DeploymentBias name: Deployment Bias def: "Arises when systems are used as decision aids for humans, since the human intermediary may act on predictions in ways that are typically not modeled in the system. However, it is still individuals using the deployed system." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/GroupBias ! Group Bias [Term] id: https://w3id.org/aio/DepthwiseConv1DLayer name: DepthwiseConv1D Layer def: "Depthwise 1D convolution. Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution. It is implemented via the following steps: Split the input into individual channels. Convolve each channel with an individual depthwise kernel with depth_multiplier output channels. Concatenate the convolved outputs along the channels axis. Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels. The depth_multiplier argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/DepthwiseConv2DLayer name: DepthwiseConv2D Layer def: "Depthwise 2D convolution." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/DetectionBias name: Detection Bias def: "Systematic differences between groups in how outcomes are determined and may cause an over- or underestimation of the size of the effect." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/DialogueLLM name: Dialogue LLM def: "A dialogue LLM is optimized for engaging in multi-turn conversations, understanding context and generating relevant, coherent responses continuously over many dialogue turns." [TBD] {type="owl:Axiom"} synonym: "conversational AI" RELATED [] synonym: "Dialogue LLM" EXACT [] synonym: "multi-turn dialogue" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/DifferentiableLLM name: Differentiable LLM def: "A differentiable LLM has an architecture amenable to full end-to-end training via backpropagation, without relying on teacher forcing or unlikelihood training objectives." [TBD] {type="owl:Axiom"} synonym: "Differentiable LLM" EXACT [] synonym: "end-to-end training" RELATED [] synonym: "fully backpropagable" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/DimensionalityReduction name: Dimensionality Reduction def: "The transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension." [https://en.wikipedia.org/wiki/Dimensionality_reduction] {type="owl:Axiom"} synonym: "Dimension Reduction" EXACT [] is_a: https://w3id.org/aio/UnsupervisedLearning ! Unsupervised Learning [Term] id: https://w3id.org/aio/DiscretizationLayer name: Discretization Layer def: "A preprocessing layer which buckets continuous features by ranges." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Discretization] {type="owl:Axiom"} is_a: https://w3id.org/aio/NumericalFeaturesPreprocessingLayer ! Numerical Features Preprocessing Layer [Term] id: https://w3id.org/aio/Distillation name: Distillation def: "Knowledge distillation involves training a smaller model to replicate the behavior of a larger model, aiming to compress the knowledge into a more compact form without significant loss of performance." [https://doi.org/10.48550/arXiv.2105.13093] {type="owl:Axiom"} synonym: "Knowledge compression" RELATED [] synonym: "Purification" EXACT [] synonym: "Refining" EXACT [] synonym: "Teacher-student model" RELATED [] is_a: https://w3id.org/aio/ModelEfficiency ! Model Efficiency [Term] id: https://w3id.org/aio/Domain-AdaptedLLM name: Domain-Adapted LLM def: "A domain-adapted LLM is first pre-trained on a broad corpus, then fine-tuned on domain-specific data to specialize its capabilities for particular domains or applications, like scientific literature or code generation." [TBD] {type="owl:Axiom"} synonym: "domain robustness" RELATED [] synonym: "Domain-Adapted LLM" EXACT [] synonym: "transfer learning" RELATED [] is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/DotLayer name: Dot Layer def: "Layer that computes a dot product between samples in two tensors. E.g. if applied to a list of two tensors a and b of shape (batch_size, n), the output will be a tensor of shape (batch_size, 1) where each entry i will be the dot product between a[i] and b[i]." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dot] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/DropoutLayer name: Dropout Layer def: "Applies Dropout to the input. The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over all inputs is unchanged. Note that the Dropout layer only applies when training is set to True such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically, and in other contexts, you can set the kwarg explicitly to True when calling the layer. (This is in contrast to setting trainable=False for a Dropout layer. trainable does not affect the layer's behavior, as Dropout does not have any variables/weights that can be frozen during training.)" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/Dunning-KrugerEffectBias name: Dunning-Kruger Effect Bias def: "The tendency of people with low ability in a given area or task to overestimate their self-assessed ability. Typically measured by comparing self-assessment with objective performance, often called subjective ability and objective ability, respectively." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Dunning-Kruger Effect" EXACT [] is_a: https://w3id.org/aio/CognitiveBias ! Cognitive Bias [Term] id: https://w3id.org/aio/ELUFunction name: ELU Function def: "The exponential linear unit (ELU) with alpha > 0 is: x if x > 0 and alpha * (exp(x) - 1) if x < 0 The ELU hyperparameter alpha controls the value to which an ELU saturates for negative net inputs. ELUs diminish the vanishing gradient effect. ELUs have negative values which pushes the mean of the activations closer to zero. Mean activations that are closer to zero enable faster Learning as they bring the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument gets smaller. Saturation means a small derivative which decreases the variation and the information that is propagated to the next layer." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/elu] {type="owl:Axiom"} synonym: "ELU" EXACT [] synonym: "Exponential Linear Unit" EXACT [] is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/ELULayer name: ELU Layer def: "Exponential Linear Unit." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ELU] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/EchoStateNetwork name: Echo State Network def: "The echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or reproduce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system." [https://en.wikipedia.org/wiki/Echo_state_network#\:~\:text=The%20echo%20state%20network%20(ESN\,are%20fixed%20and%20randomly%20assigned] {type="owl:Axiom"} comment: Input, Recurrent, Output synonym: "ESN" EXACT [] is_a: https://w3id.org/aio/RecurrentNeuralNetwork ! Recurrent Neural Network [Term] id: https://w3id.org/aio/EcologicalFallacyBias name: Ecological Fallacy Bias def: "Occurs when an inference is made about an individual based on their membership within a group." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Ecological Fallacy" EXACT [] is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/EmbeddingLayer name: Embedding Layer def: "Turns positive integers (indexes) into dense vectors of fixed size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/EmbodiedLLM name: Embodied LLM def: "An embodied LLM integrates language with other modalities like vision, audio, robotics to enable grounded language understanding in real-world environments." [TBD] {type="owl:Axiom"} synonym: "Embodied LLM" EXACT [] synonym: "multimodal grounding" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/EmergentBias name: Emergent Bias def: "Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/Encoder-DecoderLLM name: Encoder-Decoder LLM def: "The Encoder-Decoder architecture was the original transformer architecture introduced in the Attention Is All You Need (https://arxiv.org/abs/1706.03762) paper. The encoder processes the input sequence and generates a hidden representation that summarizes the input information. The decoder uses this hidden representation to generate the desired output sequence. The encoder and decoder are trained end-to-end to maximize the likelihood of the correct output sequence given the input sequence." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder%2Donly&text=These%20models%20have%20a%20pre\,Named%20entity%20recognition] {type="owl:Axiom"} synonym: "LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/EncoderLLM name: Encoder LLM def: "The Encoder-only architecture is used when only encoding the input sequence is required and the decoder is not necessary. The input sequence is encoded into a fixed-length representation and then used as input to a classifier or a regressor to make a prediction. These models have a pre-trained general-purpose encoder but will require fine-tuning of the final classifier or regressor." [https://www.practicalai.io/understanding-transformer-model-architectures/#\:~\:text=Encoder%2Donly&text=These%20models%20have%20a%20pre\,Named%20entity%20recognition] {type="owl:Axiom"} synonym: "LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/Energy-BasedLLM name: Energy-Based LLM def: "An energy-based LLM models the explicit probability density over token sequences using an energy function, rather than an autoregressive factorization. This can improve modeling of long-range dependencies and global coherence." [TBD] {type="owl:Axiom"} synonym: "energy scoring" RELATED [] synonym: "Energy-Based LLM" EXACT [] synonym: "explicit density modeling" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/EnhancementStrategies name: Enhancement Strategies def: "An abstract parent class grouping LLMs based on model enhancement strategies." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/LargeLanguageModel ! Large Language Model [Term] id: https://w3id.org/aio/EnsembleLearning name: Ensemble Learning def: "Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone." [https://en.wikipedia.org/wiki/Ensemble_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/ErrorPropagationBias name: Error Propagation Bias def: "The effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Error Propagation" EXACT [] is_a: https://w3id.org/aio/ProcessingBias ! Processing Bias [Term] id: https://w3id.org/aio/EthicalLLM name: Ethical LLM def: "An ethical LLM is trained to uphold certain ethical principles, values or rules in its language generation to increase safety and trustworthiness." [TBD] {type="owl:Axiom"} synonym: "constituitional AI" RELATED [] synonym: "Ethical LLM" EXACT [] synonym: "value alignment" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/EvaluationBias name: Evaluation Bias def: "Arises when the testing or external benchmark populations do not equally represent the various parts of the user population or from the use of performance metrics that are not appropriate for the way in which the model will be used." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/EvolutionaryLLM name: Evolutionary LLM def: "An evolutionary LLM applies principles of evolutionary computation to optimize its structure and parameters, evolving over time to improve performance." [TBD] {type="owl:Axiom"} synonym: "evolutionary algorithms" RELATED [] synonym: "Evolutionary Language Model" EXACT [] synonym: "genetic programming" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/ExclusionBias name: Exclusion Bias def: "When specific groups of user populations are excluded from testing and subsequent analyses." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/ExplainableLLM name: Explainable LLM def: "An explainable LLM is designed to provide insights into its decision-making process, making it easier for users to understand and trust the model's outputs. It incorporates mechanisms for interpreting and explaining its predictions in human-understandable terms." [TBD] {type="owl:Axiom"} synonym: "Explainable Language Model" EXACT [] synonym: "interpretability" RELATED [] synonym: "model understanding" RELATED [] synonym: "XAI LLM" EXACT [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/ExponentialFunction name: Exponential Function def: "The exponential function is a mathematical function denoted by f(x)=exp or e^{x}." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/exponential] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/ExtremeLearningMachine name: Extreme Learning Machine def: "Extreme Learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature Learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned. These hidden nodes can be randomly assigned and never updated (i.e. they are random projection but with nonlinear transforms), or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to Learning a linear model. (https://en.wikipedia.org/wiki/Extreme_Learning_machine)" [https://en.wikipedia.org/wiki/Extreme_Learning_machine] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "ELM" EXACT [] is_a: https://w3id.org/aio/FeedbackNetwork ! Feedback Network [Term] id: https://w3id.org/aio/FactorizedLLM name: Factorized LLM def: "A factorized LLM decomposes the full language modeling task into multiple sub-components or experts that each focus on a subset of the information. This enables more efficient scaling." [TBD] {type="owl:Axiom"} synonym: "Conditional masking" RELATED [] synonym: "Factorized LLM" EXACT [] synonym: "Product of experts" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/FeatureExtraction name: Feature Extraction def: "Extracting specific features or patterns from the text before training to guide the model's learning process, including syntactic information or semantic embeddings." [TBD] {type="owl:Axiom"} synonym: "Attribute Extraction" EXACT [] synonym: "Feature Isolation" EXACT [] synonym: "Semantic embeddings" RELATED [] synonym: "Syntactic information" RELATED [] is_a: https://w3id.org/aio/DataEnhancement ! DataEnhancement [Term] id: https://w3id.org/aio/FederatedLLM name: Federated LLM def: "A federated LLM is trained in a decentralized manner across multiple devices or silos, without directly sharing private data. This enables collaborative training while preserving data privacy and security." [TBD] {type="owl:Axiom"} synonym: "decentralized training" RELATED [] synonym: "Federated LLM" EXACT [] synonym: "privacy-preserving" RELATED [] is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/FederatedLearning name: Federated Learning def: "A technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them." [https://en.wikipedia.org/wiki/Federated_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/FeedbackLoopBias name: Feedback Loop Bias def: "Effects that may occur when an algorithm learns from user behavior and feeds that behavior back into the model." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/FeedbackNetwork name: Feedback Network def: "A feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration's output. (https://arxiv.org/abs/1612.09508)" [TBD] {type="owl:Axiom"} comment: Input, Hidden, Output, Hidden synonym: "FBN" EXACT [] is_a: https://w3id.org/aio/ArtificialNeuralNetwork ! Artificial Neural Network [Term] id: https://w3id.org/aio/FixedEffectsModel name: Fixed Effects Model def: "A statistical model in which the model parameters are fixed or non-random quantities." [https://en.wikipedia.org/wiki/Fixed_effects_model] {type="owl:Axiom"} synonym: "FEM" EXACT [] is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/FlattenLayer name: Flatten Layer def: "Flattens the input. Does not affect the batch size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/FractionalMaxPool2DLayer name: FractionalMaxPool2D Layer def: "Applies a 2D fractional max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "FractionalMaxPool2D" EXACT [] synonym: "FractionalMaxPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/FractionalMaxPool3DLayer name: FractionalMaxPool3D Layer def: "Applies a 3D fractional max pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "FractionalMaxPool3D" EXACT [] synonym: "FractionalMaxPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/Function name: Function def: "Function parent class" [TBD] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/FundingBias name: Funding Bias def: "Arises when biased results are reported in order to support or satisfy the funding agency or financial supporter of the research study, but it can also be the individual researcher." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/GroupBias ! Group Bias [Term] id: https://w3id.org/aio/GELUFunction name: GELU Function def: "Gaussian error linear unit (GELU) computes x * P(X <= x), where P(X) ~ N(0, 1). The (GELU) nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLU." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/gelu] {type="owl:Axiom"} synonym: "Gaussian Error Linear Unit" EXACT [] synonym: "GELU" EXACT [] is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/GRUCellLayer name: GRUCell Layer def: "Cell class for the GRU layer. This class processes one step within the whole time sequence input, whereas tf.keras.layer.GRU processes the whole sequence." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRUCell] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/GRULayer name: GRU Layer def: "Gated Recurrent Unit - Cho et al. 2014. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: activation == tanh, recurrent_activation == sigmoid, recurrent_dropout == 0, unroll is False, use_bias is True, reset_after is True. Inputs, if use masking, are strictly right-padded. Eager execution is enabled in the outermost context. There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel and recurrent_kernel. To use this variant, set reset_after=True and recurrent_activation='sigmoid'." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/GatedRecurrentUnit name: Gated Recurrent Unit def: "Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM.[4][5] GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets." [https://en.wikipedia.org/wiki/Gated_recurrent_unit] {type="owl:Axiom"} comment: Input, Memory Cell, Output synonym: "GRU" EXACT [] is_a: https://w3id.org/aio/LongShortTermMemory ! Long Short Term Memory [Term] id: https://w3id.org/aio/GaussianDropoutLayer name: GaussianDropout Layer def: "Apply multiplicative 1-centered Gaussian noise. As it is a regularization layer, it is only active at training time." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GaussianDropout] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/GaussianNoiseLayer name: GaussianNoise Layer def: "Apply additive zero-centered Gaussian noise. This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GaussianNoise] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/GeneralizedFew-shotLearning name: Generalized Few-shot Learning def: "Methods that can learn novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes." [https://paperswithcode.com/paper/generalized-and-incremental-few-shot-learning/review/] {type="owl:Axiom"} synonym: "GFSL" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/GeneralizedLinearModel name: Generalized Linear Model def: "This model generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value." [https://en.wikipedia.org/wiki/Generalized_linear_model] {type="owl:Axiom"} synonym: "GLM" EXACT [] is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/GenerativeAdversarialNetwork name: Generative Adversarial Network def: "A generative adversarial network (GAN) is a class of machine Learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised Learning, GANs have also proven useful for semi-supervised Learning, fully supervised Learning,[ and reinforcement Learning. The core idea of a GAN is based on the \"indirect\" training through the discriminator,[clarification needed] which itself is also being updated dynamically. This basically means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner." [https://en.wikipedia.org/wiki/Generative_adversarial_network] {type="owl:Axiom"} comment: Backfed Input, Hidden, Matched Output-Input, Hidden, Matched Output-Input synonym: "GAN" EXACT [] is_a: https://w3id.org/aio/UnsupervisedPretrainedNetwork ! Unsupervised Pretrained Network [Term] id: https://w3id.org/aio/GenerativeAdversarialNetwork-AugmentedLLM name: Generative Adversarial Network-Augmented LLM def: "A GAN-augmented LLM incorporates a generative adversarial network (GAN) into its training process, using a discriminator network to provide a signal for generating more realistic and coherent text. This adversarial training can improve the quality and diversity of generated text." [TBD] {type="owl:Axiom"} synonym: "adversarial training" RELATED [] synonym: "GAN-LLM" EXACT [] synonym: "Generative Adversarial Network-Augmented LLM" EXACT [] synonym: "text generation" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/GenerativeCommonsenseLLM name: Generative Commonsense LLM def: "A generative commonsense LLM is trained to understand and model basic physics, causality and common sense about how the real world works." [TBD] {type="owl:Axiom"} synonym: "causal modeling" RELATED [] synonym: "Generative Commonsense LLM" EXACT [] synonym: "physical reasoning" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/GenerativeLanguageInterface name: Generative Language Interface def: "A generative language interface enables users to engage in an interactive dialogue with an LLM, providing feedback to guide and refine the generated outputs iteratively." [TBD] {type="owl:Axiom"} synonym: "Generative Language Interface" EXACT [] synonym: "Interactive generation" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/GlobalAveragePooling1DLayer name: GlobalAveragePooling1D Layer def: "Global average pooling operation for temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D] {type="owl:Axiom"} synonym: "GlobalAvgPool1D" EXACT [] synonym: "GlobalAvgPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GlobalAveragePooling2DLayer name: GlobalAveragePooling2D Layer def: "Global average pooling operation for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling2D] {type="owl:Axiom"} synonym: "GlobalAvgPool2D" EXACT [] synonym: "GlobalAvgPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GlobalAveragePooling3DLayer name: GlobalAveragePooling3D Layer def: "Global Average pooling operation for 3D data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling3D] {type="owl:Axiom"} synonym: "GlobalAvgPool3D" EXACT [] synonym: "GlobalAvgPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GlobalMaxPooling1DLayer name: GlobalMaxPooling1D Layer def: "Global max pooling operation for 1D temporal data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool1D] {type="owl:Axiom"} synonym: "GlobalMaxPool1D" EXACT [] synonym: "GlobalMaxPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GlobalMaxPooling2DLayer name: GlobalMaxPooling2D Layer def: "Global max pooling operation for spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool2D] {type="owl:Axiom"} synonym: "GlobalMaxPool2D" EXACT [] synonym: "GlobalMaxPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GlobalMaxPooling3DLayer name: GlobalMaxPooling3D Layer def: "Global Max pooling operation for 3D data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalMaxPool3D] {type="owl:Axiom"} synonym: "GlobalMaxPool3D" EXACT [] synonym: "GlobalMaxPool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/GraphConvolutionalNetwork name: Graph Convolutional Network def: "GCN is a type of convolutional neural network that can work directly on graphs and take advantage of their structural information. (https://arxiv.org/abs/1609.02907)" [https://arxiv.org/abs/1609.02907] {type="owl:Axiom"} comment: Input, Hidden, Hidden, Output synonym: "GCN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/GraphConvolutionalPolicyNetwork name: Graph Convolutional Policy Network def: "Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement Learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules." [https://arxiv.org/abs/1806.02473] {type="owl:Axiom"} comment: Input, Hidden, Hidden, Policy, Output synonym: "GPCN" EXACT [] is_a: https://w3id.org/aio/GraphConvolutionalNetwork ! Graph Convolutional Network [Term] id: https://w3id.org/aio/GraphLLM name: Graph LLM def: "A graph LLM operates over structured inputs/outputs represented as graphs, enabling reasoning over explicit relational knowledge representations during language tasks." [https://doi.org/10.48550/arXiv.2311.12399] {type="owl:Axiom"} synonym: "Graph LLM" EXACT [] synonym: "Structured representations" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/GroupBias name: Group Bias def: "A pattern of favoring members of one's in-group over out-group members. This can be expressed in evaluation of others, in allocation of resources, and in many other ways." [https://en.wikipedia.org/wiki/In-group_favoritism] {type="owl:Axiom"} synonym: "In-group bias" EXACT [] synonym: "In-group Favoritism" EXACT [] synonym: "In-group preference" EXACT [] synonym: "In-group–out-group Bias" EXACT [] synonym: "Intergroup bias" EXACT [] is_a: https://w3id.org/aio/HumanBias ! Human Bias [Term] id: https://w3id.org/aio/GroupNormLayer name: GroupNorm Layer def: "Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization" [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "GroupNorm" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/GroupthinkBias name: Groupthink Bias def: "A psychological phenomenon that occurs when people in a group tend to make non-optimal decisions based on their desire to conform to the group, or fear of dissenting with the group. In groupthink, individuals often refrain from expressing their personal disagreement with the group, hesitating to voice opinions that do not align with the group." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Groupthink" EXACT [] is_a: https://w3id.org/aio/GroupBias ! Group Bias [Term] id: https://w3id.org/aio/HardSigmoidFunction name: Hard Sigmoid Function def: "A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid'" [https://www.tensorflow.org/api_docs/python/tf/keras/activations/hard_sigmoid] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/HashingLayer name: Hashing Layer def: "A preprocessing layer which hashes and bins categorical features. This layer transforms categorical inputs to hashed output. It element-wise converts a ints or strings to ints in a fixed range. The stable hash function uses tensorflow::ops::Fingerprint to produce the same output consistently across all platforms. This layer uses FarmHash64 by default, which provides a consistent hashed output across different platforms and is stable across invocations, regardless of device and context, by mixing the input bits thoroughly. If you want to obfuscate the hashed output, you can also pass a random salt argument in the constructor. In that case, the layer will use the SipHash64 hash function, with the salt value serving as additional input to the hash function." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Hashing] {type="owl:Axiom"} is_a: https://w3id.org/aio/CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer [Term] id: https://w3id.org/aio/HiddenLayer name: Hidden Layer def: "A hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network. Hidden layers vary depending on the function of the neural network, and similarly, the layers may vary depending on their associated weights." [https://deepai.org/machine-Learning-glossary-and-terms/hidden-layer-machine-Learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/HierarchicalClassification name: Hierarchical Classification def: "Methods that group things according to a hierarchy." [https://en.wikipedia.org/wiki/Hierarchical_classification] {type="owl:Axiom"} is_a: https://w3id.org/aio/Classification ! Classification [Term] id: https://w3id.org/aio/HierarchicalClustering name: Hierarchical Clustering def: "Methods that seek to build a hierarchy of clusters." [https://en.wikipedia.org/wiki/Hierarchical_clustering] {type="owl:Axiom"} synonym: "HCL" EXACT [] is_a: https://w3id.org/aio/Clustering ! Clustering [Term] id: https://w3id.org/aio/HierarchicalLLM name: Hierarchical LLM def: "A hierarchical LLM models language at multiple levels of granularity, learning hierarchical representations that can capture both low-level patterns and high-level abstractions." [TBD] {type="owl:Axiom"} synonym: "Hierarchical LLM" EXACT [] synonym: "multi-scale representations" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/HistoricalBias name: Historical Bias def: "Referring to the long-standing biases encoded in society over time. Related to, but distinct from, biases in historical description, or the interpretation, analysis, and explanation of history. A common example of historical bias is the tendency to view the larger world from a Western or European view." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/HopfieldNetwork name: Hopfield Network def: "A Hopfield network is a form of recurrent artificial neural network and a type of spin glass system popularised by John Hopfield in 1982 as described earlier by Little in 1974 based on Ernst Ising's work with Wilhelm Lenz on the Ising model. Hopfield networks serve as content-addressable (\"associative\") memory systems with binary threshold nodes, or with continuous variables. Hopfield networks also provide a model for understanding human memory. (https://en.wikipedia.org/wiki/Hopfield_network)" [https://en.wikipedia.org/wiki/Hopfield_network] {type="owl:Axiom"} comment: Backfed input synonym: "HN" EXACT [] synonym: "Ising model of a neural network" EXACT [] synonym: "Ising–Lenz–Little model" EXACT [] is_a: https://w3id.org/aio/SymmetricallyConnectedNetwork ! Symmetrically Connected Network [Term] id: https://w3id.org/aio/HostileAttributionBias name: Hostile Attribution Bias def: "A bias wherein individuals perceive benign or ambiguous behaviors as hostile." [https://en.wikipedia.org/wiki/Interpretive_bias] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/HumanBias name: Human Bias def: "Systematic errors in human thought based on a limited number of heuristic principles and predicting values to simpler judgmental operations." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/HumanReportingBias name: Human Reporting Bias def: "When users rely on automation as a heuristic replacement for their own information seeking and processing." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ImageAugmentationLayer name: Image Augmentation Layer def: "A layer that performs image data preprocessing augmentations." [https://keras.io/guides/preprocessing_layers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ImagePreprocessingLayer name: Image Preprocessing Layer def: "A layer that performs image data preprocessing operations." [https://keras.io/guides/preprocessing_layers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ImplicitBias name: Implicit Bias def: "An unconscious belief, attitude, feeling, association, or stereotype that can affect the way in which humans process information, make decisions, and take actions." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Confirmatory Bias" EXACT [] is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ImplicitLanguageModel name: Implicit Language Model def: "An implicit language model uses an energy function to score full sequences instead of factorizing probabilities autoregressively. This can better capture global properties and long-range dependencies." [TBD] {type="owl:Axiom"} synonym: "Energy-based models" RELATED [] synonym: "Implicit Language Model" EXACT [] synonym: "Token-level scoring" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/IncremenetalFew-shotLearning name: Incremenetal Few-shot Learning def: "Methods that train a network on a base set of classes and then is presented several novel classes, each with only a few labeled examples." [https://arxiv.org/abs/1810.07218] {type="owl:Axiom"} synonym: "IFSL" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/IndividualBias name: Individual Bias def: "Individual bias is a persistent point of view or limited list of such points of view that one applies (\"parent\", \"academic\", \"professional\", or etc.)." [https://develop.consumerium.org/wiki/Individual_bias] {type="owl:Axiom"} is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/InheritedBias name: Inherited Bias def: "Arises when applications that are built with machine Learning are used to generate inputs for other machine Learning algorithms. If the output is biased in any way, this bias may be inherited by systems using the output as input to learn other models." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/ProcessingBias ! Processing Bias [Term] id: https://w3id.org/aio/InputLayer name: Input Layer def: "The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network." [https://www.techopedia.com/definition/33262/input-layer-neural-networks#\:~\:text=Explains%20Input%20Layer-\,What%20Does%20Input%20Layer%20Mean%3F\,for%20the%20artificial%20neural%20network.] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/InputLayerLayer name: InputLayer Layer def: "Layer to be used as an entry point into a Network (a graph of layers)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/InputSpecLayer name: InputSpec Layer def: "Specifies the rank, dtype and shape of every input to a layer. Layers can expose (if appropriate) an input_spec attribute: an instance of InputSpec, or a nested structure of InputSpec instances (one per input tensor). These objects enable the layer to run input compatibility checks for input structure, input rank, input shape, and input dtype. A None entry in a shape is compatible with any dimension, a None shape is compatible with any shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputSpec] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/InstanceNorm1dLayer name: InstanceNorm1d Layer def: "Applies Instance Normalization over a 2D (unbatched) or 3D (batched) input as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "InstanceNorm1D" EXACT [] synonym: "InstanceNorm1d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/InstanceNorm2d name: InstanceNorm2d def: "Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "InstanceNorm2D" EXACT [] synonym: "InstanceNorm2d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/InstanceNorm3dLayer name: InstanceNorm3d Layer def: "Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "InstanceNorm3D" EXACT [] synonym: "InstanceNorm3d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/InstitutionalBias name: Institutional Bias def: "In contrast to biases exhibited at the level of individual persons, institutional bias refers to a tendency exhibited at the level of entire institutions, where practices or norms result in the favoring or disadvantaging of certain social groups. Common examples include institutional racism and institutional sexism." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/Instruction-TunedLLM name: Instruction-Tuned LLM def: "An instruction-tuned LLM is fine-tuned to follow natural language instructions accurately and safely, learning to map from instructions to desired model behavior in a more controlled and principled way." [TBD] {type="owl:Axiom"} synonym: "constitutional AI" RELATED [] synonym: "Instruction-Tuned LLM" EXACT [] synonym: "natural language instructions" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/IntegerLookupLayer name: IntegerLookup Layer def: "A preprocessing layer which maps integer features to contiguous ranges." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/IntegerLookup] {type="owl:Axiom"} is_a: https://w3id.org/aio/CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer [Term] id: https://w3id.org/aio/InterfaceandIntegration name: Interface and Integration def: "An abstract parent class grouping LLMs based on model interfaces and integration." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/LargeLanguageModel ! Large Language Model [Term] id: https://w3id.org/aio/InterpretabilityandEthics name: Interpretability and Ethics def: "An abstract parent class grouping LLMs based on model interpretability and ethics." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/LargeLanguageModel ! Large Language Model [Term] id: https://w3id.org/aio/InterpretableLLM name: Interpretable LLM def: "An interpretable LLM prioritizes transparency and ease of understanding in its operations, making its decision-making processes clear and rational to human users." [TBD] {type="owl:Axiom"} synonym: "interpretability" RELATED [] synonym: "Interpretable Language Model" EXACT [] synonym: "model transparency" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/InterpretationBias name: Interpretation Bias def: "A form of information processing bias that can occur when users interpret algorithmic outputs according to their internalized biases and views." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/K-nearestNeighborAlgorithm name: K-nearest Neighbor Algorithm def: "An algorithm to group objects by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors" [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm] {type="owl:Axiom"} synonym: "K-NN" EXACT [] synonym: "KNN" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/K-nearestNeighborClassificationAlgorithm name: K-nearest Neighbor Classification Algorithm def: "An algorithm to classify objects by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors" [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm] {type="owl:Axiom"} synonym: "K-NN" EXACT [] synonym: "KNN" EXACT [] is_a: https://w3id.org/aio/Classification ! Classification is_a: https://w3id.org/aio/Clustering ! Clustering [Term] id: https://w3id.org/aio/K-nearestNeighborRegressionAlgorithm name: K-nearest Neighbor Regression Algorithm def: "An algorithm to assign the average of the values of k nearest neighbors to objects." [https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm] {type="owl:Axiom"} synonym: "K-NN" EXACT [] synonym: "KNN" EXACT [] is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/Knowledge-GroundedLLM name: Knowledge-Grounded LLM def: "A knowledge-grounded LLM incorporates external knowledge sources or knowledge bases into the model architecture, enabling it to generate more factually accurate and knowledge-aware text." [TBD] {type="owl:Axiom"} synonym: "factual grounding" RELATED [] synonym: "knowledge integration" RELATED [] synonym: "Knowledge-Grounded LLM" EXACT [] is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/KnowledgeTransfer name: Knowledge Transfer def: "Starting the training from a model already trained on a related task to reduce training time and improve performance on tasks with limited data." [https://doi.org/10.1016/j.knosys.2015.01.010] {type="owl:Axiom"} synonym: "Adaptation" RELATED [] synonym: "Inductive Transfer" EXACT [] synonym: "Pretrained models" RELATED [] synonym: "Skill Acquisition" EXACT [] is_a: https://w3id.org/aio/TrainingStrategies ! Training Strategies [Term] id: https://w3id.org/aio/KohonenNetwork name: Kohonen Network def: "A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine Learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example, a data set with p variables measured in n observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a two-dimensional \"map\" such that observations in proximal clusters have more similar values than observations in distal clusters. This can make high-dimensional data easier to visualize and analyze. An SOM is a type of artificial neural network but is trained using competitive Learning rather than the error-correction Learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. The SOM was introduced by the Finnish professor Teuvo Kohonen in the 1980s and therefore is sometimes called a Kohonen map or Kohonen network.[1][2] The Kohonen map or network is a computationally convenient abstraction building on biological models of neural systems from the 1970s[3] and morphogenesis models dating back to Alan Turing in the 1950s." [https://en.wikipedia.org/wiki/Self-organizing_map] {type="owl:Axiom"} comment: Input, Hidden synonym: "KN" EXACT [] synonym: "Self-Organizing Feature Map" EXACT [] synonym: "Self-Organizing Map" EXACT [] synonym: "SOFM" EXACT [] synonym: "SOM" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/LPPool1DLayer name: LPPool1D Layer def: "Applies a 1D power-average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "LPPool1D" EXACT [] synonym: "LPPool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/LPPool2DLayer name: LPPool2D Layer def: "Applies a 2D power-average pooling over an input signal composed of several input planes." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "LPPool2D" EXACT [] synonym: "LPPool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/LSTMCellLayer name: LSTMCell Layer def: "Cell class for the LSTM layer." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTMCell] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/LSTMLayer name: LSTM Layer def: "Long Short-Term Memory layer - Hochreiter 1997. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation. The requirements to use the cuDNN implementation are: 1. activation == tanh, 2. recurrent_activation == sigmoid, 3. recurrent_dropout == 0, 4. unroll is False, 5. use_bias is True, 6. Inputs, if use masking, are strictly right-padded, 7. Eager execution is enabled in the outermost context." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/LambdaLayer name: Lambda Layer def: "Wraps arbitrary expressions as a Layer object." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/LanguageInterfaceLLM name: Language Interface LLM def: "A language interface LLM supports interactive semantic parsing, enabling users to provide feedback/corrections which are used to dynamically refine and update the language model." [TBD] {type="owl:Axiom"} synonym: "Interactive learning" RELATED [] synonym: "Language Interface LLM" EXACT [] is_a: https://w3id.org/aio/InterfaceandIntegration ! Interface and Integration [Term] id: https://w3id.org/aio/LargeLanguageModel name: Large Language Model def: "A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning." [https://en.wikipedia.org/wiki/Large_language_model] {type="owl:Axiom"} synonym: "LLM" EXACT [] [Term] id: https://w3id.org/aio/LassoRegression name: Lasso Regression def: "A regression analysis method that performs both variable selection and regularizationin order to enhance the prediction accuracy and interpretability of the resulting statistical model." [https://en.wikipedia.org/wiki/Lasso_(statistics)] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/Layer name: Layer def: "Network layer parent class" [TBD] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/LayerLayer name: Layer Layer def: "This is the class from which all layers inherit. A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables). State can be created in various places, at the convenience of the subclass implementer: in __init__(); in the optional build() method, which is invoked by the first __call__() to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time; in the first invocation of call(), with some caveats discussed below. Users will just instantiate a layer and then treat it as a callable." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/LayerNormLayer name: LayerNorm Layer def: "Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization" [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LayerNorm" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/LayerNormalizationLayer name: LayerNormalization Layer def: "Layer normalization layer (Ba et al., 2016). Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1. Given a tensor inputs, moments are calculated and normalization is performed across the axes specified in axis." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LayerNormalization] {type="owl:Axiom"} is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/LazyBatchNorm1DLayer name: LazyBatchNorm1D Layer def: "A torch.nn.BatchNorm1d module with lazy initialization of the num_features argument of the BatchNorm1d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyBatchNorm1D" EXACT [] synonym: "LazyBatchNorm1d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/LazyBatchNorm2DLayer name: LazyBatchNorm2D Layer def: "A torch.nn.BatchNorm2d module with lazy initialization of the num_features argument of the BatchNorm2d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyBatchNorm2D" EXACT [] synonym: "LazyBatchNorm2d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/LazyBatchNorm3DLayer name: LazyBatchNorm3D Layer def: "A torch.nn.BatchNorm3d module with lazy initialization of the num_features argument of the BatchNorm3d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyBatchNorm3D" EXACT [] synonym: "LazyBatchNorm3d" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/LazyInstanceNorm1dLayer name: LazyInstanceNorm1d Layer def: "A torch.nn.InstanceNorm1d module with lazy initialization of the num_features argument of the InstanceNorm1d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyInstanceNorm1D" EXACT [] synonym: "LazyInstanceNorm1d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/LazyInstanceNorm2dLayer name: LazyInstanceNorm2d Layer def: "A torch.nn.InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyInstanceNorm2D" EXACT [] synonym: "LazyInstanceNorm2d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/LazyInstanceNorm3dLayer name: LazyInstanceNorm3d Layer def: "A torch.nn.InstanceNorm3d module with lazy initialization of the num_features argument of the InstanceNorm3d that is inferred from the input.size(1)." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LazyInstanceNorm3D" EXACT [] synonym: "LazyInstanceNorm3d" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/LeakyReLULayer name: LeakyReLU Layer def: "Leaky version of a Rectified Linear Unit." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/LearningParadigms name: Learning Paradigms def: "An abstract parent class grouping LLMs based on model learning paradigms." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/Least-squaresAnalysis name: Least-squares Analysis def: "A standard approach in regression analysis to approximate the solution of overdetermined systems(sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation." [https://en.wikipedia.org/wiki/Least_squares] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/LifelongLearningLLM name: Lifelong Learning LLM def: "A lifelong learning LLM can continually acquire new knowledge over time without forgetting previously learned information, maintaining a balance between plasticity and stability." [TBD] {type="owl:Axiom"} synonym: "Catastrophic forgetting" RELATED [] synonym: "Continual Learning LLM" EXACT [] synonym: "Lifelong Learning LLM" EXACT [] synonym: "Plasticity-Stability balance" RELATED [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/LinearFunction name: Linear Function def: "A linear function has the form f(x) = a + bx." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/linear] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/LinearRegression name: Linear Regression def: "A linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables)." [https://en.wikipedia.org/wiki/Linear_regression] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/LinkingBias name: Linking Bias def: "Arises when network attributes obtained from user connections, activities, or interactions differ and misrepresent the true behavior of the users." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/UseAndInterpretationBias ! Use And Interpretation Bias [Term] id: https://w3id.org/aio/LiquidStateMachineNetwork name: Liquid State Machine Network def: "A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units. The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision. The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples). (https://en.wikipedia.org/wiki/Liquid_state_machine)" [https://en.wikipedia.org/wiki/Liquid_state_machine] {type="owl:Axiom"} comment: Input, Spiking Hidden, Output synonym: "LSM" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/LocalResponseNormLayer name: LocalResponseNorm Layer def: "Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "LocalResponseNorm" EXACT [] is_a: https://w3id.org/aio/NormalizationLayer ! Normalization Layer [Term] id: https://w3id.org/aio/Locally-connectedLayer name: Locally-connected Layer def: "The LocallyConnected1D layer works similarly to the Convolution1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input." [https://faroit.com/keras-docs/1.2.2/layers/local/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/LocallyConnected1DLayer name: LocallyConnected1D Layer def: "Locally-connected layer for 1D inputs. The LocallyConnected1D layer works similarly to the Conv1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LocallyConnected1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Locally-connectedLayer ! Locally-connected Layer [Term] id: https://w3id.org/aio/LocallyConnected2DLayer name: LocallyConnected2D Layer def: "Locally-connected layer for 2D inputs. The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/LocallyConnected2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Locally-connectedLayer ! Locally-connected Layer [Term] id: https://w3id.org/aio/LogisticRegression name: Logistic Regression def: "A statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables." [https://en.wikipedia.org/wiki/Logistic_regression] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/LongShortTermMemory name: Long Short Term Memory def: "Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep Learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell." [https://en.wikipedia.org/wiki/Long_short-term_memory] {type="owl:Axiom"} comment: Input, Memory Cell, Output synonym: "LSTM" EXACT [] is_a: https://w3id.org/aio/RecurrentNeuralNetwork ! Recurrent Neural Network [Term] id: https://w3id.org/aio/LossOfSituationalAwarenessBias name: Loss Of Situational Awareness Bias def: "When automation leads to humans being unaware of their situation such that, when control of a system is given back to them in a situation where humans and machines cooperate, they are unprepared to assume their duties. This can be a loss of awareness over what automation is and isn’t taking care of." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/Low-ResourceLLM name: Low-Resource LLM def: "A low-resource LLM is optimized for performance in scenarios with limited data, computational resources, or for languages with sparse datasets." [TBD] {type="owl:Axiom"} synonym: "Low-Resource Language Model" EXACT [] synonym: "low-resource languages" RELATED [] synonym: "resource-efficient" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/MachineLearning name: Machine Learning def: "A field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks." [https://en.wikipedia.org/wiki/Machine_learning] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/ManifoldLearning name: Manifold Learning def: "Methods based on the assumption that one's observed data lie on a low-dimensional manifold embedded in a higher-dimensional space." [https://arxiv.org/abs/2011.01307] {type="owl:Axiom"} is_a: https://w3id.org/aio/DimensionalityReduction ! Dimensionality Reduction [Term] id: https://w3id.org/aio/MarkovChain name: Markov Chain def: "A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.[1][2][3] A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC). It is named after the Russian mathematician Andrey Markov." [https://en.wikipedia.org/wiki/Markov_chain] {type="owl:Axiom"} comment: Probalistic Hidden synonym: "Markov Process" EXACT [] synonym: "MC" EXACT [] synonym: "MP" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/MaskedLanguageModel name: Masked Language Model def: "A masked language model is a type of language model that is trained to predict randomly masked tokens in a sequence, based on the remaining unmasked tokens. This allows it to build deep bidirectional representations that can be effectively transferred to various NLP tasks via fine-tuning." [TBD] {type="owl:Axiom"} synonym: "bidirectional encoder" RELATED [] synonym: "denoising autoencoder" RELATED [] synonym: "Masked Language Model" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/MaskingLayer name: Masking Layer def: "Masks a sequence by using a mask value to skip timesteps. For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to mask_value, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking). If any downstream layer does not support masking yet receives such an input mask, an exception will be raised." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Masking] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/MaxPooling1DLayer name: MaxPooling1D Layer def: "Max pooling operation for 1D temporal data. Downsamples the input representation by taking the maximum value over a spatial window of size pool_size. The window is shifted by strides. The resulting output, when using the \"valid\" padding option, has a shape of: output_shape = (input_shape - pool_size + 1) / strides) The resulting output shape when using the \"same\" padding option is: output_shape = input_shape / strides." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool1D] {type="owl:Axiom"} synonym: "MaxPool1D" EXACT [] synonym: "MaxPool1d" EXACT [] synonym: "MaxPooling1D" EXACT [] synonym: "MaxPooling1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaxPooling2DLayer name: MaxPooling2D Layer def: "Max pooling operation for 2D spatial data." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D] {type="owl:Axiom"} synonym: "MaxPool2D" EXACT [] synonym: "MaxPool2d" EXACT [] synonym: "MaxPooling2D" EXACT [] synonym: "MaxPooling2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaxPooling3DLayer name: MaxPooling3D Layer def: "Max pooling operation for 3D data (spatial or spatio-temporal). Downsamples the input along its spatial dimensions (depth, height, and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input. The window is shifted by strides along each dimension." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool3D] {type="owl:Axiom"} synonym: "MaxPool3D" EXACT [] synonym: "MaxPool3d" EXACT [] synonym: "MaxPooling3D" EXACT [] synonym: "MaxPooling3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaxUnpool1DLayer name: MaxUnpool1D Layer def: "Computes a partial inverse of MaxPool1d." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "MaxUnpool1D" EXACT [] synonym: "MaxUnpool1d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaxUnpool2DLayer name: MaxUnpool2D Layer def: "Computes a partial inverse of MaxPool2d." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "MaxUnpool2D" EXACT [] synonym: "MaxUnpool2d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaxUnpool3DLayer name: MaxUnpool3D Layer def: "Computes a partial inverse of MaxPool3d." [https://pytorch.org/docs/stable/nn.html#pooling-layers] {type="owl:Axiom"} synonym: "MaxUnpool3D" EXACT [] synonym: "MaxUnpool3d" EXACT [] is_a: https://w3id.org/aio/PoolingLayer ! Pooling Layer [Term] id: https://w3id.org/aio/MaximumLayer name: Maximum Layer def: "Layer that computes the maximum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Maximum] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/MeasurementBias name: Measurement Bias def: "Arises when features and labels are proxies for desired quantities, potentially leaving out important factors or introducing group or input-dependent noise that leads to differential performance." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/Memory-AugmentedLLM name: Memory-Augmented LLM def: "A memory-augmented LLM incorporates external writeable and readable memory components, allowing it to store and retrieve information over long contexts." [TBD] {type="owl:Axiom"} synonym: "external memory" RELATED [] synonym: "Memory-Augmented LLM" EXACT [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/MergingLayer name: Merging Layer def: "A layer used to merge a list of inputs." [https://www.tutorialspoint.com/keras/keras_merge_layer.htm] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Meta-Learning name: Meta-Learning def: "Automatic learning algorithms applied to metadata about machine Learning experiments." [https://en.wikipedia.org/wiki/Meta_learning_(computer_science)] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/Meta-LearningLLM name: Meta-Learning LLM def: "A meta-learning LLM is trained in a way that allows it to quickly adapt to new tasks or datasets through only a few examples or fine-tuning steps, leveraging meta-learned priors about how to efficiently learn." [TBD] {type="owl:Axiom"} synonym: "few-shot adaptation" RELATED [] synonym: "learning to learn" RELATED [] synonym: "Meta-Learning LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/MetricLearning name: Metric Learning def: "Methods which can learn a representation function that maps objects into an embedded space." [https://paperswithcode.com/task/metric-learning] {type="owl:Axiom"} synonym: "Distance Metric Learning" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/MinimumLayer name: Minimum Layer def: "Layer that computes the minimum (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Minimum] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/Mixture-of-ExpertsLLM name: Mixture-of-Experts LLM def: "A Mixture-of-Experts LLM dynamically selects and combines outputs from multiple expert submodels, allowing for efficient scaling by conditionally activating only a subset of model components for each input." [TBD] {type="owl:Axiom"} synonym: "conditional computation" RELATED [] synonym: "Mixture-of-Experts LLM" EXACT [] synonym: "model parallelism" RELATED [] synonym: "MoE LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/ModeConfusionBias name: Mode Confusion Bias def: "When modal interfaces confuse human operators, who misunderstand which mode the system is using, taking actions which are correct for a different mode but incorrect for their current situation. This is the cause of many deadly accidents, but also a source of confusion in everyday life." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/ModelArchitecture name: Model Architecture def: "An abstract parent class grouping LLMs based on model architecture." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/LargeLanguageModel ! Large Language Model [Term] id: https://w3id.org/aio/ModelEfficiency name: Model Efficiency def: "Techniques aimed at making models more efficient, such as knowledge distillation." [https://doi.org/10.1145/3578938] {type="owl:Axiom"} synonym: "Computational Efficiency" EXACT [] synonym: "Model Optimization" EXACT [] is_a: https://w3id.org/aio/Preprocessing ! Preprocessing [Term] id: https://w3id.org/aio/ModelSelectionBias name: Model Selection Bias def: "The bias introduced while using the data to select a single seemingly “best” model from a large set of models employing many predictor variables. Model selection bias also occurs when an explanatory variable has a weak relationship with the response variable." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/ProcessingBias ! Processing Bias [Term] id: https://w3id.org/aio/ModularLLM name: Modular LLM def: "A modular LLM consists of multiple specialized components or skills that can be dynamically composed and recombined to solve complex tasks, mimicking the modular structure of human cognition." [TBD] {type="owl:Axiom"} synonym: "component skills" RELATED [] synonym: "Modular LLM" EXACT [] synonym: "skill composition" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/Multi-TaskLLM name: Multi-Task LLM def: "A multi-task LLM is trained jointly on multiple language tasks simultaneously, learning shared representations that transfer across tasks." [TBD] {type="owl:Axiom"} synonym: "Multi-Task LLM" EXACT [] synonym: "transfer learning" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/MultiHeadAttentionLayer name: MultiHeadAttention Layer def: "MultiHeadAttention layer. This is an implementation of multi-headed attention as described in the paper \"Attention is all you Need\" (Vaswani et al., 2017). If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.This layer first projects query, key and value. These are (effectively) a list of tensors of length num_attention_heads, where the corresponding shapes are (batch_size, , key_dim), (batch_size, , key_dim), (batch_size, , value_dim).Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor. Finally, the result tensor with the last dimension as value_dim can take an linear projection and return. When using MultiHeadAttention inside a custom Layer, the custom Layer must implement build() and call MultiHeadAttention's _build_from_signature(). This enables weights to be restored correctly when the model is loaded." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention] {type="owl:Axiom"} is_a: https://w3id.org/aio/AttentionLayer ! Attention Layer [Term] id: https://w3id.org/aio/MulticlassClassification name: Multiclass Classification def: "Methods that lassify instances into one of three or more classes (classifying instances into one of two classes is called binary classification)." [https://en.wikipedia.org/wiki/Multiclass_classification] {type="owl:Axiom"} synonym: "Multinomial Classification" EXACT [] is_a: https://w3id.org/aio/Classification ! Classification [Term] id: https://w3id.org/aio/MultidimensionalScaling name: Multidimensional Scaling def: "A method that translates information about the pairwise distances among a set of objects or individuals into a configuration of points mapped into an abstract Cartesian space." [https://en.wikipedia.org/wiki/Multidimensional_scaling] {type="owl:Axiom"} synonym: "MDS" EXACT [] is_a: https://w3id.org/aio/DimensionalityReduction ! Dimensionality Reduction [Term] id: https://w3id.org/aio/MultilingualLLM name: Multilingual LLM def: "A multilingual LLM is trained on text from multiple languages, learning shared representations that enable zero-shot or few-shot transfer to new languages." [TBD] {type="owl:Axiom"} synonym: "cross-lingual transfer" RELATED [] synonym: "Multilingual LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/MultimodalDeepLearning name: Multimodal Deep Learning def: "Methods which can create models that can process and link information using various modalities." [https://arxiv.org/abs/2105.11087] {type="owl:Axiom"} is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/MultimodalFusionLLM name: Multimodal Fusion LLM def: "A multimodal fusion LLM learns joint representations across different modalities like text, vision and audio in an end-to-end fashion for better cross-modal understanding and generation." [TBD] {type="owl:Axiom"} synonym: "cross-modal grounding" RELATED [] synonym: "Multimodal Fusion LLM" EXACT [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus is_a: https://w3id.org/aio/EnhancementStrategies ! Enhancement Strategies [Term] id: https://w3id.org/aio/MultimodalLearning name: Multimodal Learning def: "Methods which can represent the joint representations of different modalities." [TBD] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/MultimodalTransformer name: Multimodal Transformer def: "A multimodal transformer is a transformer architecture that can process and relate information from different modalities, such as text, images, and audio. It uses a shared embedding space and attention mechanism to learn joint representations across modalities." [TBD] {type="owl:Axiom"} synonym: "Multimodal Transformer" EXACT [] synonym: "unified encoder" RELATED [] synonym: "vision-language model" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/MultiplyLayer name: Multiply Layer def: "Layer that multiplies (element-wise) a list of inputs. It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Multiply] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/NaturalLanguageProcessing name: Natural Language Processing def: "A subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data." [https://en.wikipedia.org/wiki/Natural_language_processing] {type="owl:Axiom"} synonym: "NLP" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/Network name: Network def: "Network parent class" [TBD] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/NeuralTuringMachineNetwork name: Neural Turing Machine Network def: "A Neural Turing machine (NTMs) is a recurrent neural network model. The approach was published by Alex Graves et al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent. An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone." [https://en.wikipedia.org/wiki/Neural_Turing_machine] {type="owl:Axiom"} comment: Input, Hidden, Spiking Hidden, Output synonym: "NTM" EXACT [] is_a: https://w3id.org/aio/DeepFeedForward ! Deep FeedForward is_a: https://w3id.org/aio/LongShortTermMemory ! Long Short Term Memory [Term] id: https://w3id.org/aio/Neuro-SymbolicLLM name: Neuro-Symbolic LLM def: "A neuro-symbolic LLM combines neural language modeling with symbolic reasoning components, leveraging structured knowledge representations and logical inferences to improve reasoning capabilities." [TBD] {type="owl:Axiom"} synonym: "knowledge reasoning" RELATED [] synonym: "Neuro-Symbolic LLM" EXACT [] synonym: "symbolic grounding" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/NoiseDenseLayer name: Noise Dense Layer def: "Noisy dense layer that injects random noise to the weights of dense layer. Noisy dense layers are fully connected layers whose weights and biases are augmented by factorised Gaussian noise. The factorised Gaussian noise is controlled through gradient descent by a second weights layer. A NoisyDense layer implements the operation: $$ mathrm{NoisyDense}(x) = mathrm{activation}(mathrm{dot}(x, mu + (sigma cdot epsilon)) mathrm{bias}) $$ where mu is the standard weights layer, epsilon is the factorised Gaussian noise, and delta is a second weights layer which controls epsilon." [https://www.tensorflow.org/addons/api_docs/python/tfa/layers/NoisyDense] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/NormalizationLayer name: Normalization Layer def: "A preprocessing layer which normalizes continuous features." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Normalization] {type="owl:Axiom"} is_a: https://w3id.org/aio/NumericalFeaturesPreprocessingLayer ! Numerical Features Preprocessing Layer [Term] id: https://w3id.org/aio/NumericalFeaturesPreprocessingLayer name: Numerical Features Preprocessing Layer def: "A layer that performs numerical data preprocessing operations." [https://keras.io/guides/preprocessing_layers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/One-shotLearning name: One-shot Learning def: "A method which aims to classify objects from one, or only a few, examples." [https://en.wikipedia.org/wiki/One-shot_learning] {type="owl:Axiom"} synonym: "OSL" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/OrdinalLLM name: Ordinal LLM def: "An ordinal LLM is trained to model ordinal relationships and rank outputs, rather than model probability distributions over text sequences directly." [TBD] {type="owl:Axiom"} synonym: "Ordinal LLM" EXACT [] synonym: "preference modeling" RELATED [] synonym: "ranking" RELATED [] is_a: https://w3id.org/aio/InterpretabilityandEthics ! Interpretability and Ethics [Term] id: https://w3id.org/aio/OutputLayer name: Output Layer def: "The output layer in an artificial neural network is the last layer of neurons that produces given outputs for the program. Though they are made much like other artificial neurons in the neural network, output layer neurons may be built or observed in a different way, given that they are the last “actor” nodes on the network." [https://www.techopedia.com/definition/33263/output-layer-neural-networks] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/PReLULayer name: PReLU Layer def: "Parametric Rectified Linear Unit." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/PReLU] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/Perceptron name: Perceptron def: "The perceptron is an algorithm for supervised Learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. (https://en.wikipedia.org/wiki/Perceptron)" [TBD] {type="owl:Axiom"} comment: Input, Output synonym: "Single Layer Perceptron" EXACT [] synonym: "SLP" EXACT [] is_a: https://w3id.org/aio/ArtificialNeuralNetwork ! Artificial Neural Network [Term] id: https://w3id.org/aio/PermuteLayer name: Permute Layer def: "Permutes the dimensions of the input according to a given pattern. Useful e.g. connecting RNNs and convnets." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Permute] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/PersonalizedLLM name: Personalized LLM def: "A personalized LLM adapts its language modeling and generation to the preferences, style and persona of individual users or audiences." [TBD] {type="owl:Axiom"} synonym: "Personalized LLM" EXACT [] synonym: "user adaptation LLM" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/PoolingLayer name: Pooling Layer def: "Pooling layers serve the dual purposes of mitigating the sensitivity of convolutional layers to location and of spatially downsampling representations." [https://d2l.ai/chapter_convolutional-neural-networks/pooling.html] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/PopularityBias name: Popularity Bias def: "A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/PopulationBias name: Population Bias def: "A form of selection bias that occurs when items that are more popular are more exposed and less popular items are under-represented.aSystematic distortions in demographics or other user characteristics between a population of users represented in a dataset or on a platform and some target population." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/Preprocessing name: Preprocessing def: "A range of techniques and processes applied to data before it is used in machine learning models or AI algorithms" [https://doi.org/10.1109/ICDE.2019.00245] {type="owl:Axiom"} [Term] id: https://w3id.org/aio/PreprocessingLayer name: Preprocessing Layer def: "A layer that performs data preprocessing operations." [https://www.tensorflow.org/guide/keras/preprocessing_layers] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/PresentationBias name: Presentation Bias def: "Biases arising from how information is presented on the Web, via a user interface, due to rating or ranking of output, or through users’ own self-selected, biased interaction." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/PrincipalComponentAnalysis name: Principal Component Analysis def: "A method for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data." [https://en.wikipedia.org/wiki/Principal_component_analysis] {type="owl:Axiom"} synonym: "PCA" EXACT [] is_a: https://w3id.org/aio/DimensionalityReduction ! Dimensionality Reduction [Term] id: https://w3id.org/aio/ProbabilisticGraphicalModel name: Probabilistic Graphical Model def: "A probabilistic model for which a graph expresses the conditional dependence structure between random variables." [https://en.wikipedia.org/wiki/Graphical_model] {type="owl:Axiom"} synonym: "Graphical Model" EXACT [] synonym: "PGM" EXACT [] synonym: "Structure Probabilistic Model" EXACT [] is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/ProbabilisticTopicModel name: Probabilistic Topic Model def: "Methods that use statistical methods to analyze the words in each text to discover common themes, how those themes are connected to each other, and how they change over time." [https://pyro.ai/examples/prodlda.html] {type="owl:Axiom"} is_a: https://w3id.org/aio/ProbabilisticGraphicalModel ! Probabilistic Graphical Model [Term] id: https://w3id.org/aio/ProcessingBias name: Processing Bias def: "Judgement modulated by affect, which is influenced by the level of efficacy and efficiency in information processing; in cognitive sciences, processing bias is often referred to as an aesthetic judgement." [https://royalsocietypublishing.org/doi/10.1098/rspb.2019.0165#d1e5237] {type="owl:Axiom"} synonym: "Validation Bias" EXACT [] is_a: https://w3id.org/aio/ComputationalBias ! Computational Bias [Term] id: https://w3id.org/aio/Prompt-basedFine-TuningLLM name: Prompt-based Fine-Tuning LLM def: "A prompt-tuned LLM is fine-tuned on a small number of examples or prompts, rather than full task datasets. This allows for rapid adaptation to new tasks with limited data, leveraging the model's few-shot learning capabilities." [TBD] {type="owl:Axiom"} synonym: "few-shot learning" RELATED [] synonym: "in-context learning" RELATED [] synonym: "Prompt-based Fine-Tuning LLM" EXACT [] synonym: "Prompt-tuned LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/ProportionalHazardsModel name: Proportional Hazards Model def: "A surival modeling method where the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate." [https://en.wikipedia.org/wiki/Proportional_hazards_model] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis is_a: https://w3id.org/aio/SurvivalAnalysis ! Survival Analysis [Term] id: https://w3id.org/aio/RNNLayer name: RNN Layer def: "Base class for recurrent layers." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RNN] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/RadialBasisNetwork name: Radial Basis Network def: "Like recurrent neural networks (RNNs), transformers are designed to handle sequential input data, such as natural language, for tasks such as translation and text summarization. However, unlike RNNs, transformers do not necessarily process the data in order. Rather, the attention mechanism provides context for any position in the input sequence." [https://en.wikipedia.org/wiki/Radial_basis_function_network] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "Radial Basis Function Network" EXACT [] synonym: "RBFN" EXACT [] synonym: "RBN" EXACT [] is_a: https://w3id.org/aio/DeepFeedForward ! Deep FeedForward [Term] id: https://w3id.org/aio/RandomBrightnessLayer name: RandomBrightness Layer def: "A preprocessing layer which randomly adjusts brightness during training. This layer will randomly increase/reduce the brightness for the input RGB images. At inference time, the output will be identical to the input. Call the layer with training=True to adjust the brightness of the input. Note that different brightness adjustment factors will be apply to each the images in the batch." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomBrightness] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomContrastLayer name: RandomContrast Layer def: "A preprocessing layer which randomly adjusts contrast during training. This layer will randomly adjust the contrast of an image or images by a random factor. Contrast is adjusted independently for each channel of each image during training. For each channel, this layer computes the mean of the image pixels in the channel and then adjusts each component x of each pixel to (x - mean) * contrast_factor + mean. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and in integer or floating point dtype. By default, the layer will output floats. The output value will be clipped to the range [0, 255], the valid range of RGB colors." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomContrast] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomCropLayer name: RandomCrop Layer def: "A preprocessing layer which randomly crops images during training. During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location. At inference time, and during training if an input image is smaller than the target size, the input will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. If you need to apply random cropping at inference time, set training to True when calling the layer. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomEffectsModel name: Random Effects Model def: "A statistical model where the model parameters are random variables." [https://en.wikipedia.org/wiki/Random_effects_model] {type="owl:Axiom"} synonym: "REM" EXACT [] is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/RandomFlipLayer name: RandomFlip Layer def: "A preprocessing layer which randomly flips images during training. This layer will flip the images horizontally and or vertically based on the mode attribute. During inference time, the output will be identical to input. Call the layer with training=True to flip the input. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomForest name: Random Forest def: "An ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time." [https://en.wikipedia.org/wiki/Random_forest] {type="owl:Axiom"} is_a: https://w3id.org/aio/EnsembleLearning ! Ensemble Learning [Term] id: https://w3id.org/aio/RandomHeightLayer name: RandomHeight Layer def: "A preprocessing layer which randomly varies image height during training. This layer adjusts the height of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the \"channels_last\" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomRotationLayer name: RandomRotation Layer def: "A preprocessing layer which randomly rotates images during training." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomTranslationLayer name: RandomTranslation Layer def: "A preprocessing layer which randomly translates images during training. This layer will apply random translations to each image during training, filling empty space according to fill_mode. aInput pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomWidthLayer name: RandomWidth Layer def: "A preprocessing layer which randomly varies image width during training. This layer will randomly adjusts the width of a batch of images of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the \"channels_last\" image data format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RandomZoomLayer name: RandomZoom Layer def: "A preprocessing layer which randomly zooms images during training. This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to fill_mode.Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RankingBias name: Ranking Bias def: "The idea that top-ranked results are the most relevant and important and will result in more clicks than other results." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/AnchoringBias ! Anchoring Bias [Term] id: https://w3id.org/aio/RashomonEffectBias name: Rashomon Effect Bias def: "Refers to differences in perspective, memory and recall, interpretation, and reporting on the same event from multiple persons or witnesses." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Rashomon Effect" EXACT [] synonym: "Rashomon Principle" EXACT [] is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/RationalLLM name: Rational LLM def: "A rational LLM incorporates explicit reasoning capabilities, leveraging logical rules, axioms or external knowledge to make deductive inferences during language tasks." [TBD] {type="owl:Axiom"} synonym: "logical inferences" RELATED [] synonym: "Rational LLM" EXACT [] synonym: "reasoning" RELATED [] is_a: https://w3id.org/aio/ApplicationFocus ! Application Focus [Term] id: https://w3id.org/aio/ReLUFunction name: ReLU Function def: "The ReLU activation function returns: max(x, 0), the element-wise maximum of 0 and the input tensor." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu] {type="owl:Axiom"} synonym: "Rectified Linear Unit" EXACT [] synonym: "ReLU" EXACT [] is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/ReLULayer name: ReLU Layer def: "Rectified Linear Unit activation function. With default values, it returns element-wise max(x, 0)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ReLU] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/RecurrentLayer name: Recurrent Layer def: "A layer of an RNB, composed of recurrent units and with the number of which is the hidden size of the layer." [https://docs.nvidia.com/deepLearning/performance/dl-performance-recurrent/index.html#recurrent-layer] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/RecurrentNeuralNetwork name: Recurrent Neural Network def: "A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs." [https://en.wikipedia.org/wiki/Recurrent_neural_network] {type="owl:Axiom"} comment: Input, Memory Cell, Output synonym: "RecNN" EXACT [] synonym: "Recurrent Network" EXACT [] synonym: "RN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/RecursiveLLM name: Recursive LLM def: "A recursive or self-attending LLM incorporates recursive self-attention mechanisms, allowing it to iteratively refine its own outputs and capture long-range dependencies more effectively." [TBD] {type="owl:Axiom"} synonym: "iterative refinement" RELATED [] synonym: "Recursive LLM" EXACT [] synonym: "Self-Attending LLM" EXACT [] synonym: "self-attention" RELATED [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/RecursiveLanguageModel name: Recursive Language Model def: "A recursive language model uses recursive neural network architectures like TreeLSTMs to learn syntactic composition functions, improving systematic generalization abilities." [https://doi.org/10.1609/aaai.v33i01.33017450] {type="owl:Axiom"} synonym: "Compositional generalization" RELATED [] synonym: "RLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/RecursiveNeuralNetwork name: Recursive Neural Network def: "A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order. Recursive neural networks, sometimes abbreviated as RvNNs, have been successful, for instance, in Learning sequence and tree structures in natural language processing, mainly phrase and sentence continuous representations based on word embedding." [https://en.wikipedia.org/wiki/Recursive_neural_network] {type="owl:Axiom"} synonym: "RecuNN" EXACT [] synonym: "RvNN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/RegressionAnalysis name: Regression Analysis def: "A set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features')." [https://en.wikipedia.org/wiki/Regression_analysis] {type="owl:Axiom"} synonym: "Regression analysis" EXACT [] synonym: "Regression model" EXACT [] is_a: https://w3id.org/aio/SupervisedLearning ! Supervised Learning [Term] id: https://w3id.org/aio/RegularizationLayer name: Regularization Layer def: "Regularizers allow you to apply penalties on layer parameters or layer activity during optimization. These penalties are summed into the loss function that the network optimizes. Regularization penalties are applied on a per-layer basis." [https://keras.io/api/layers/regularizers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ReinforcementLearning name: Reinforcement Learning def: "Methods that do not need labelled input/output pairs be presented, nor needing sub-optimal actions to be explicitly corrected. Instead they focus on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)." [https://en.wikipedia.org/wiki/Reinforcement_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/ReinforcementLearningLLM name: Reinforcement Learning LLM def: "An RL-LLM is a language model that is fine-tuned using reinforcement learning, where the model receives rewards for generating text that satisfies certain desired properties or objectives. This can improve the quality, safety, or alignment of generated text." [TBD] {type="owl:Axiom"} synonym: "decision transformers" RELATED [] synonym: "Reinforcement Learning LLM" EXACT [] synonym: "reward modeling" RELATED [] synonym: "RL-LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/RepeatVectorLayer name: RepeatVector Layer def: "Repeats the input n times." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/RepeatVector] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/RepresentationBias name: Representation Bias def: "Arises due to non-random sampling of subgroups, causing trends estimated for one population to not be generalizable to data collected from a new population." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/RepresentationLearning name: Representation Learning def: "Methods that allow a system to discover the representations required for feature detection or classification from raw data." [https://en.wikipedia.org/wiki/Feature_learning] {type="owl:Axiom"} synonym: "Feature Learning" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/RescalingLayer name: Rescaling Layer def: "A preprocessing layer which rescales input values to a new range." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling] {type="owl:Axiom"} is_a: https://w3id.org/aio/ImagePreprocessingLayer ! Image Preprocessing Layer [Term] id: https://w3id.org/aio/ReshapeLayer name: Reshape Layer def: "Layer that reshapes inputs into the given shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Reshape] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/ReshapingLayer name: Reshaping Layer def: "Reshape layers are used to change the shape of the input." [https://keras.io/api/layers/reshaping_layers/reshape/] {type="owl:Axiom"} synonym: "Reshape Layer" EXACT [] is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/ResidualNeuralNetwork name: Residual Neural Network def: "A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers. Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. An additional weight matrix may be used to learn the skip weights; these models are known as HighwayNets. Models with several parallel skips are referred to as DenseNets. In the context of residual neural networks, a non-residual network may be described as a 'plain network'." [https://en.wikipedia.org/wiki/Residual_neural_network] {type="owl:Axiom"} comment: Input, Weight, BN, ReLU, Weight, BN, Addition, ReLU synonym: "Deep Residual Network" EXACT [] synonym: "DRN" EXACT [] synonym: "ResNet" EXACT [] synonym: "ResNN" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/ResizingLayer name: Resizing Layer def: "A preprocessing layer which resizes images. This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in \"channels_last\" format. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of interger or floating point dtype. By default, the layer will output floats. This layer can be called on tf.RaggedTensor batches of input images of distinct sizes, and will resize the outputs to dense tensors of uniform size." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Resizing] {type="owl:Axiom"} is_a: https://w3id.org/aio/ImagePreprocessingLayer ! Image Preprocessing Layer [Term] id: https://w3id.org/aio/RestrictedBoltzmannMachine name: Restricted Boltzmann Machine def: "A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs." [https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine] {type="owl:Axiom"} comment: Backfed Input, Probabilistic Hidden synonym: "RBM" EXACT [] is_a: https://w3id.org/aio/BoltzmannMachineNetwork ! Boltzmann Machine Network [Term] id: https://w3id.org/aio/Retrieval-AugmentedLLM name: Retrieval-Augmented LLM def: "A retrieval-augmented LLM combines a pre-trained language model with a retrieval system that can access external knowledge sources. This allows the model to condition its generation on relevant retrieved knowledge, improving factual accuracy and knowledge grounding." [TBD] {type="owl:Axiom"} synonym: "knowledge grounding" RELATED [] synonym: "open-book question answering" RELATED [] synonym: "Retrieval-Augmented LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/RidgeRegression name: Ridge Regression def: "A method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated.[1] It has been used in many fields including econometrics, chemistry, and engineering." [https://en.wikipedia.org/wiki/Ridge_regression] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/SELUFunction name: SELU Function def: "The SELU activation function multiplies scale (> 1) with the output of the ELU function to ensure a slope larger than one for positive inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/selu] {type="owl:Axiom"} synonym: "Scaled Exponential Linear Unit" EXACT [] synonym: "SELU" EXACT [] is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SelectionAndSamplingBias name: Selection And Sampling Bias def: "Bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed." [https://en.wikipedia.org/wiki/Selection_bias] {type="owl:Axiom"} synonym: "Sampling Bias" EXACT [] synonym: "Selection Bias" EXACT [] synonym: "Selection Effect" EXACT [] is_a: https://w3id.org/aio/ComputationalBias ! Computational Bias [Term] id: https://w3id.org/aio/SelectiveAdherenceBias name: Selective Adherence Bias def: "Decision-makers’ inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/Self-SupervisedLLM name: Self-Supervised LLM def: "A self-supervised LLM learns rich representations by solving pretext tasks that involve predicting parts of the input from other observed parts of the data, without relying on human-annotated labels." [TBD] {type="owl:Axiom"} synonym: "Pretext tasks" RELATED [] synonym: "Self-Supervised LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/Self-supervisedLearning name: Self-supervised Learning def: "Regarded as an intermediate form between supervised and unsupervised learning." [https://en.wikipedia.org/wiki/Self-supervised_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/Semi-SupervisedLLM name: Semi-Supervised LLM def: "A semi-supervised LLM combines self-supervised pretraining on unlabeled data with supervised fine-tuning on labeled task data." [TBD] {type="owl:Axiom"} synonym: "self-training" RELATED [] synonym: "Semi-Supervised LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/SeparableConvolution1DLayer name: SeparableConvolution1D Layer def: "Depthwise separable 1D convolution. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If use_bias is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.a" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SeparableConv1D] {type="owl:Axiom"} synonym: "SeparableConv1D Layer" EXACT [] is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/SeparableConvolution2DLayer name: SeparableConvolution2D Layer def: "Depthwise separable 2D convolution. Separable convolutions consist of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes the resulting output channels. The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SeparableConv2D] {type="owl:Axiom"} synonym: "SeparableConv2D Layer" EXACT [] is_a: https://w3id.org/aio/ConvolutionalLayer ! Convolutional Layer [Term] id: https://w3id.org/aio/SigmoidFunction name: Sigmoid Function def: "Applies the sigmoid activation function sigmoid(x) = 1 / (1 + exp(-x)). For small values (<-5), sigmoid returns a value close to zero, and for large values (>5) the result of the function gets close to 1. Sigmoid is equivalent to a 2-element Softmax, where the second element is assumed to be zero. The sigmoid function always returns a value between 0 and 1." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SimpleRNNCellLayer name: SimpleRNNCell Layer def: "Cell class for SimpleRNN. This class processes one step within the whole time sequence input, whereas tf.keras.layer.SimpleRNN processes the whole sequence." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNNCell] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/SimpleRNNLayer name: SimpleRNN Layer def: "Fully-connected RNN where the output is to be fed back to input." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNN] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/Simpon'sParadoxBias name: Simpon's Paradox Bias def: "A statistical phenomenon where the marginal association between two categorical variables is qualitatively different from the partial association between the same two variables after controlling for one or more other variables. For example, the statistical association or correlation that has been detected between two variables for an entire population disappears or reverses when the population is divided into subgroups." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Simpson's Paradox" EXACT [] is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/SocietalBias name: Societal Bias def: "Can be positive or negative, and take a number of different forms, but is typically characterized as being for or against groups or individuals based on social identities, demographic factors, or immutable physical characteristics. Societal or social biases are often stereotypes. Common examples of societal or social biases are based on concepts like race, ethnicity, gender, sexual orientation, socioeconomic status, education, and more. Societal bias is often recognized and discussed in the context of NLP (Natural Language Processing) models." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Social Bias" EXACT [] is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/SoftmaxFunction name: Softmax Function def: "The elements of the output vector are in range (0, 1) and sum to 1. Each vector is handled independently. The axis argument sets which axis of the input the function is applied along. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of each vector x is computed as exp(x) / tf.reduce_sum(exp(x)). The input values in are the log-odds of the resulting probability." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SoftmaxLayer name: Softmax Layer def: "Softmax activation function." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Softmax] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/SoftplusFunction name: Softplus Function def: "softplus(x) = log(exp(x) + 1)" [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softplus] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SoftsignFunction name: Softsign Function def: "softsign(x) = x / (abs(x) + 1)" [https://www.tensorflow.org/api_docs/python/tf/keras/activations/softsign] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SparseAutoEncoder name: Sparse Auto Encoder def: "Sparse autoencoders may include more (rather than fewer) hidden units than inputs, but only a small number of the hidden units are allowed to be active at the same time (thus, sparse). This constraint forces the model to respond to the unique statistical features of the training data. (https://en.wikipedia.org/wiki/Autoencoder)" [TBD] {type="owl:Axiom"} comment: Input, Hidden, Matched Output-Input synonym: "SAE" EXACT [] synonym: "Sparse AE" EXACT [] synonym: "Sparse Autoencoder" EXACT [] is_a: https://w3id.org/aio/AutoEncoderNetwork ! Auto Encoder Network [Term] id: https://w3id.org/aio/SparseLLM name: Sparse LLM def: "A sparse LLM uses techniques like pruning or quantization to reduce the number of non-zero parameters in the model, making it more parameter-efficient and easier to deploy on resource-constrained devices." [TBD] {type="owl:Axiom"} synonym: "model compression" RELATED [] synonym: "parameter efficiency" RELATED [] synonym: "Sparse LLM" EXACT [] is_a: https://w3id.org/aio/ModelArchitecture ! Model Architecture [Term] id: https://w3id.org/aio/SparseLearning name: Sparse Learning def: "Methods which aim to find sparse representations of the input data in the form of a linear combination of basic elements as well as those basic elements themselves." [https://en.wikipedia.org/wiki/Sparse_dictionary_learning] {type="owl:Axiom"} synonym: "Sparse coding" EXACT [] synonym: "Sparse dictionary Learning" EXACT [] is_a: https://w3id.org/aio/RepresentationLearning ! Representation Learning [Term] id: https://w3id.org/aio/SpatialDropout1DLayer name: SpatialDropout1D Layer def: "Spatial 1D version of Dropout. This version performs the same function as Dropout, however, it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/SpatialDropout2DLayer name: SpatialDropout2D Layer def: "Spatial 2D version of Dropout. This version performs the same function as Dropout, however, it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.a" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/SpatialDropout3DLayer name: SpatialDropout3D Layer def: "Spatial 3D version of Dropout. This version performs the same function as Dropout, however, it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective Learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout3D] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegularizationLayer ! Regularization Layer [Term] id: https://w3id.org/aio/SpatialRegression name: Spatial Regression def: "Regression method used to model spatial relationships." [https://gisgeography.com/spatial-regression-models-arcgis/] {type="owl:Axiom"} is_a: https://w3id.org/aio/RegressionAnalysis ! Regression Analysis [Term] id: https://w3id.org/aio/StackedRNNCellsLayer name: StackedRNNCells Layer def: "Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/StackedRNNCells] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/StreetlightEffectBias name: Streetlight Effect Bias def: "A bias whereby people tend to search only where it is easiest to look." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Streetlight Effect" EXACT [] is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/StringLookupLayer name: StringLookup Layer def: "A preprocessing layer which maps string features to integer indices." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/StringLookup] {type="owl:Axiom"} is_a: https://w3id.org/aio/CategoricalFeaturesPreprocessingLayer ! Categorical Features Preprocessing Layer [Term] id: https://w3id.org/aio/SubtractLayer name: Subtract Layer def: "Layer that subtracts two inputs. It takes as input a list of tensors of size 2, both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]), also of the same shape." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Subtract] {type="owl:Axiom"} is_a: https://w3id.org/aio/MergingLayer ! Merging Layer [Term] id: https://w3id.org/aio/SubwordSegmentation name: Subword Segmentation def: "Utilizing techniques like Byte Pair Encoding (BPE) or SentencePiece to break down words into smaller units, allowing the model to handle a wide range of vocabulary with a fixed-size list." [TBD] {type="owl:Axiom"} synonym: "Byte Pair Encoding" RELATED [] synonym: "Fragmentation" EXACT [] synonym: "Part-word Division" EXACT [] synonym: "SentencePiece" RELATED [] is_a: https://w3id.org/aio/DataPreparation ! Data Preparation [Term] id: https://w3id.org/aio/SunkCostFallacyBias name: Sunk Cost Fallacy Bias def: "A human tendency where people opt to continue with an endeavor or behavior due to previously spent or invested resources, such as money, time, and effort, regardless of whether costs outweigh benefits. For example, in AI, the sunk cost fallacy could lead development teams and organizations to feel that because they have already invested so much time and money into a particular AI application, they must pursue it to market rather than deciding to end the effort, even in the face of significant technical debt and/or ethical debt." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Sunk Cost Fallacy" EXACT [] is_a: https://w3id.org/aio/GroupBias ! Group Bias [Term] id: https://w3id.org/aio/SupervisedBiclustering name: Supervised Biclustering def: "Methods that simultaneously cluster the rows and columns of a labeled matrix, also taking into account the data label contributions to cluster coherence." [https://en.wikipedia.org/wiki/Biclustering] {type="owl:Axiom"} synonym: "Supervised Block Clustering" EXACT [] synonym: "Supervised Co-clustering" EXACT [] synonym: "Supervised Joint Clustering" EXACT [] synonym: "Supervised Two-mode Clustering" EXACT [] synonym: "Supervised Two-way Clustering" EXACT [] is_a: https://w3id.org/aio/Biclustering ! Biclustering [Term] id: https://w3id.org/aio/SupervisedClustering name: Supervised Clustering def: "Methods that group a set of labeled objects in such a way that objects in the same group (called a cluster) are more similarly labeled (in some sense) relative to those in other groups (clusters)." [https://en.wikipedia.org/wiki/Cluster_analysis] {type="owl:Axiom"} synonym: "Cluster analysis" EXACT [] is_a: https://w3id.org/aio/Clustering ! Clustering [Term] id: https://w3id.org/aio/SupervisedLearning name: Supervised Learning def: "Methods that can learn a function that maps an input to an output based on example input-output pairs." [https://en.wikipedia.org/wiki/Supervised_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/SupportVectorMachine name: Support Vector Machine def: "In machine Learning, support-vector machines (SVMs, also support-vector networks) are supervised Learning models with associated Learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical Learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall." [https://en.wikipedia.org/wiki/Support-vector_machine] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "Supper Vector Network" EXACT [] synonym: "SVM" EXACT [] synonym: "SVN" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/SurvivalAnalysis name: Survival Analysis def: "Methods for nalyzing the expected duration of time until one event occurs, such as death in biological organisms and failure in mechanical systems." [https://en.wikipedia.org/wiki/Survival_analysis] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/SurvivorshipBias name: Survivorship Bias def: "Tendency for people to focus on the items, observations, or people that “survive” or make it past a selection process, while overlooking those that did not." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/ProcessingBias ! Processing Bias [Term] id: https://w3id.org/aio/SwishFunction name: Swish Function def: "x*sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/swish] {type="owl:Axiom"} is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/SymmetricallyConnectedNetwork name: Symmetrically Connected Network def: "Like recurrent networks, but the connections between units are symmetrical (they have the same weight in both directions)." [https://ieeexplore.ieee.org/document/287176] {type="owl:Axiom"} synonym: "SCN" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/SyncBatchNormLayer name: SyncBatchNorm Layer def: "Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ." [https://pytorch.org/docs/stable/nn.html#normalization-layers] {type="owl:Axiom"} synonym: "SyncBatchNorm" EXACT [] is_a: https://w3id.org/aio/BatchNormalizationLayer ! BatchNormalization Layer [Term] id: https://w3id.org/aio/SystemicBias name: Systemic Bias def: "Biases that result from procedures and practices of particular institutions that operate in ways which result in certain social groups being advantaged or favored and others being disadvantaged or devalued." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} synonym: "Institutional Bias" EXACT [] synonym: "Societal Bias" EXACT [] is_a: https://w3id.org/aio/Bias ! Bias [Term] id: https://w3id.org/aio/TanhFunction name: Tanh Function def: "Hyperbolic tangent activation function." [https://www.tensorflow.org/api_docs/python/tf/keras/activations/tanh] {type="owl:Axiom"} synonym: "hyperbolic tangent" EXACT [] is_a: https://w3id.org/aio/Function ! Function [Term] id: https://w3id.org/aio/TemporalBias name: Temporal Bias def: "Bias that arises from differences in populations and behaviors over time." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/TextPreprocessingLayer name: Text Preprocessing Layer def: "A layer that performs text data preprocessing operations." [https://keras.io/guides/preprocessing_layers/] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/TextVectorizationLayer name: TextVectorization Layer def: "A preprocessing layer which maps text features to integer sequences." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization] {type="owl:Axiom"} is_a: https://w3id.org/aio/TextPreprocessingLayer ! Text Preprocessing Layer [Term] id: https://w3id.org/aio/ThresholdedReLULayer name: ThresholdedReLU Layer def: "Thresholded Rectified Linear Unit." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ThresholdedReLU] {type="owl:Axiom"} is_a: https://w3id.org/aio/ActivationLayer ! Activation Layer [Term] id: https://w3id.org/aio/TimeDistributedLayer name: TimeDistributed Layer def: "This wrapper allows to apply a layer to every temporal slice of an input. Every input should be at least 3D, and the dimension of index one of the first input will be considered to be the temporal dimension. Consider a batch of 32 video samples, where each sample is a 128x128 RGB image with channels_last data format, across 10 timesteps. The batch input shape is (32, 10, 128, 128, 3). You can then use TimeDistributed to apply the same Conv2D layer to each of the 10 timesteps, independently:" [https://www.tensorflow.org/api_docs/python/tf/keras/layers/TimeDistributed] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/TimeSeriesAnalysis name: Time Series Analysis def: "Methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data." [https://en.wikipedia.org/wiki/Time_series] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/TimeSeriesForecasting name: Time Series Forecasting def: "Methods that predict future values based on previously observed values." [https://en.wikipedia.org/wiki/Time_series] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/TokenizationAndVocabularyReduction name: Tokenization And Vocabulary Reduction def: "Breaking down text data into manageable pieces called tokens and reducing the model's vocabulary to streamline processing." [TBD] {type="owl:Axiom"} synonym: "Lexical Simplification" EXACT [] synonym: "Tokenization" RELATED [] synonym: "Vocabulary Condensation" EXACT [] synonym: "Vocabulary size reduction" RELATED [] is_a: https://w3id.org/aio/DataPreparation ! Data Preparation [Term] id: https://w3id.org/aio/TrainingStrategies name: Training Strategies def: "Specific strategies or methodologies employed during model training." [TBD] {type="owl:Axiom"} synonym: "Instructional Methods" EXACT [] synonym: "Learning Techniques" EXACT [] is_a: https://w3id.org/aio/Preprocessing ! Preprocessing [Term] id: https://w3id.org/aio/TransferLearning name: Transfer Learning def: "Methods which can reuse or transfer information from previously learned tasks for the Learning of new tasks." [https://en.wikipedia.org/wiki/Transfer_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/TransferLearningLLM name: Transfer Learning LLM def: "A transfer learning LLM leverages knowledge acquired during training on one task to improve performance on different but related tasks, facilitating more efficient learning and adaptation." [TBD] {type="owl:Axiom"} synonym: "transfer learning" RELATED [] synonym: "Transfer LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/TransformerNetwork name: Transformer Network def: "A transformer is a deep Learning model that adopts the mechanism of attention, differentially weighing the significance of each part of the input data. It is used primarily in the field of natural language processing (NLP) and in computer vision (CV). (https://en.wikipedia.org/wiki/Transformer_(machine_Learning_model))" [https://en.wikipedia.org/wiki/Transformer_(machine_Learning_model)] {type="owl:Axiom"} is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/UncertaintyBias name: Uncertainty Bias def: "Arises when predictive algorithms favor groups that are better represented in the training data, since there will be less uncertainty associated with those predictions." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/SelectionAndSamplingBias ! Selection And Sampling Bias [Term] id: https://w3id.org/aio/UnitNormalizationLayer name: UnitNormalization Layer def: "Unit normalization layer. Normalize a batch of inputs so that each input in the batch has a L2 norm equal to 1 (across the axes specified in axis)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UnitNormalization] {type="owl:Axiom"} is_a: https://w3id.org/aio/RecurrentLayer ! Recurrent Layer [Term] id: https://w3id.org/aio/UnsupervisedBiclustering name: Unsupervised Biclustering def: "Methods that simultaneously cluster the rows and columns of an unlabeled input matrix." [https://en.wikipedia.org/wiki/Biclustering] {type="owl:Axiom"} synonym: "Block Clustering" EXACT [] synonym: "Co-clustering" EXACT [] synonym: "Joint Clustering" EXACT [] synonym: "Two-mode Clustering" EXACT [] synonym: "Two-way Clustering" EXACT [] is_a: https://w3id.org/aio/Biclustering ! Biclustering [Term] id: https://w3id.org/aio/UnsupervisedClustering name: Unsupervised Clustering def: "Methods that group a set of objects in such a way that objects without labels in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters)." [https://en.wikipedia.org/wiki/Cluster_analysis] {type="owl:Axiom"} synonym: "Cluster analysis" EXACT [] is_a: https://w3id.org/aio/Clustering ! Clustering [Term] id: https://w3id.org/aio/UnsupervisedLLM name: Unsupervised LLM def: "An unsupervised LLM is trained solely on unlabeled data using self-supervised objectives like masked language modeling, without any supervised fine-tuning." [TBD] {type="owl:Axiom"} synonym: "self-supervised" RELATED [] synonym: "Unsupervised LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/UnsupervisedLearning name: Unsupervised Learning def: "Algorithms that learns patterns from unlabeled data." [https://en.wikipedia.org/wiki/Unsupervised_learning] {type="owl:Axiom"} is_a: https://w3id.org/aio/MachineLearning ! Machine Learning [Term] id: https://w3id.org/aio/UnsupervisedPretrainedNetwork name: Unsupervised Pretrained Network def: "Unsupervised pre-training initializes a discriminative neural net from one which was trained using an unsupervised criterion, such as a deep belief network or a deep autoencoder. This method can sometimes help with both the optimization and the overfitting issues." [https://metacademy.org/graphs/concepts/unsupervised_pre_training#\:~\:text=Unsupervised%20pre%2Dtraining%20initializes%20a\,optimization%20and%20the%20overfitting%20issues] {type="owl:Axiom"} synonym: "UPN" EXACT [] is_a: https://w3id.org/aio/Network ! Network [Term] id: https://w3id.org/aio/UpSampling1DLayer name: UpSampling1D Layer def: "Upsampling layer for 1D inputs. Repeats each temporal step size times along the time axis." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/UpSampling2DLayer name: UpSampling2D Layer def: "Upsampling layer for 2D inputs. Repeats the rows and columns of the data by size[0] and size[1] respectively." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/UpSampling3DLayer name: UpSampling3D Layer def: "Upsampling layer for 3D inputs." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling3D] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/UseAndInterpretationBias name: Use And Interpretation Bias def: "An information-processing bias, the tendency to inappropriately analyze ambiguous stimuli, scenarios and events." [https://en.wikipedia.org/wiki/Interpretive_bias] {type="owl:Axiom"} synonym: "Interpretive Bias" EXACT [] is_a: https://w3id.org/aio/ComputationalBias ! Computational Bias [Term] id: https://w3id.org/aio/UserInteractionBias name: User Interaction Bias def: "Arises when a user imposes their own self-selected biases and behavior during interaction with data, output, results, etc." [https://doi.org/10.6028/NIST.SP.1270] {type="owl:Axiom"} is_a: https://w3id.org/aio/IndividualBias ! Individual Bias [Term] id: https://w3id.org/aio/VariationalAutoEncoder name: Variational Auto Encoder def: "Variational autoencoders are meant to compress the input information into a constrained multivariate latent distribution (encoding) to reconstruct it as accurately as possible (decoding). (https://en.wikipedia.org/wiki/Variational_autoencoder)" [TBD] {type="owl:Axiom"} comment: Input, Probabilistic Hidden, Matched Output-Input synonym: "VAE" EXACT [] is_a: https://w3id.org/aio/AutoEncoderNetwork ! Auto Encoder Network [Term] id: https://w3id.org/aio/WrapperLayer name: Wrapper Layer def: "Abstract wrapper base class. Wrappers take another layer and augment it in various ways. Do not use this class as a layer, it is only an abstract base class. Two usable wrappers are the TimeDistributed and Bidirectional wrappers." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/Wrapper] {type="owl:Axiom"} is_a: https://w3id.org/aio/Layer ! Layer [Term] id: https://w3id.org/aio/Zero-ShotLearningLLM name: Zero-Shot Learning LLM def: "A zero-shot learning LLM is able to perform tasks or understand concepts it has not explicitly been trained on, demonstrating a high degree of generalization and understanding." [TBD] {type="owl:Axiom"} synonym: "zero-shot learning" RELATED [] synonym: "Zero-Shot LLM" EXACT [] is_a: https://w3id.org/aio/LearningParadigms ! Learning Paradigms [Term] id: https://w3id.org/aio/Zero-shotLearning name: Zero-shot Learning def: "Methods where at test time, a learner observes samples from classes, which were not observed during training, and needs to predict the class that they belong to." [https://en.wikipedia.org/wiki/Zero-shot_learning] {type="owl:Axiom"} synonym: "ZSL" EXACT [] is_a: https://w3id.org/aio/DeepNeuralNetwork ! Deep Neural Network [Term] id: https://w3id.org/aio/ZeroPadding1DLayer name: ZeroPadding1D Layer def: "Zero-padding layer for 1D input (e.g. temporal sequence)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding1D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/ZeroPadding2DLayer name: ZeroPadding2D Layer def: "Zero-padding layer for 2D input (e.g. picture). This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding2D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/ZeroPadding3DLayer name: ZeroPadding3D Layer def: "Zero-padding layer for 3D data (spatial or spatio-temporal)." [https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D] {type="owl:Axiom"} is_a: https://w3id.org/aio/ReshapingLayer ! Reshaping Layer [Term] id: https://w3id.org/aio/node2vec-CBOW name: node2vec-CBOW def: "In the continuous bag-of-words architecture, the model predicts the current node from a window of surrounding context nodes. The order of context nodes does not influence prediction (bag-of-words assumption)." [https://en.wikipedia.org/wiki/Word2vec] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "CBOW" RELATED [] synonym: "N2V-CBOW" EXACT [] is_a: https://w3id.org/aio/word2vec-CBOW ! word2vec-CBOW [Term] id: https://w3id.org/aio/node2vec-SkipGram name: node2vec-SkipGram def: "In the continuous skip-gram architecture, the model uses the current node to predict the surrounding window of context nodes. The skip-gram architecture weighs nearby context nodes more heavily than more distant context nodes. (https://en.wikipedia.org/wiki/Word2vec)" [https://en.wikipedia.org/wiki/Word2vec] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "N2V-SkipGram" EXACT [] synonym: "SkipGram" RELATED [] is_a: https://w3id.org/aio/word2vec-SkipGram ! word2vec-SkipGram [Term] id: https://w3id.org/aio/t-DistributedStochasticNeighborembedding name: t-Distributed Stochastic Neighbor embedding def: "A statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map." [https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding] {type="owl:Axiom"} synonym: "t-SNE" EXACT [] synonym: "tSNE" EXACT [] is_a: https://w3id.org/aio/DimensionalityReduction ! Dimensionality Reduction [Term] id: https://w3id.org/aio/word2vec-CBOW name: word2vec-CBOW def: "In the continuous bag-of-words architecture, the model predicts the current word from a window of surrounding context words. The order of context words does not influence prediction (bag-of-words assumption). (https://en.wikipedia.org/wiki/Word2vec)" [https://en.wikipedia.org/wiki/Word2vec] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "CBOW" RELATED [] synonym: "W2V-CBOW" EXACT [] is_a: https://w3id.org/aio/ArtificialNeuralNetwork ! Artificial Neural Network [Term] id: https://w3id.org/aio/word2vec-SkipGram name: word2vec-SkipGram def: "In the continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs nearby context words more heavily than more distant context words." [https://en.wikipedia.org/wiki/Word2vec] {type="owl:Axiom"} comment: Input, Hidden, Output synonym: "SkipGram" RELATED [] synonym: "W2V-SkipGram" EXACT [] is_a: https://w3id.org/aio/ArtificialNeuralNetwork ! Artificial Neural Network [Typedef] id: BFO:0000050 name: part of def: "a core relation that holds between a part and its whole" [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000111 "is part of" xsd:string property_value: IAO:0000112 "my brain is part of my body (continuant parthood, two material entities)" xsd:string property_value: IAO:0000112 "my stomach cavity is part of my stomach (continuant parthood, immaterial entity is part of material entity)" xsd:string property_value: IAO:0000112 "this day is part of this year (occurrent parthood)" xsd:string property_value: IAO:0000116 "Everything is part of itself. Any part of any part of a thing is itself part of that thing. Two distinct things cannot be part of each other." xsd:string property_value: IAO:0000116 "Occurrents are not subject to change and so parthood between occurrents holds for all the times that the part exists. Many continuants are subject to change, so parthood between continuants will only hold at certain times, but this is difficult to specify in OWL. See http://purl.obolibrary.org/obo/ro/docs/temporal-semantics/" xsd:string property_value: IAO:0000116 "Occurrents are not subject to change and so parthood between occurrents holds for all the times that the part exists. Many continuants are subject to change, so parthood between continuants will only hold at certain times, but this is difficult to specify in OWL. See https://code.google.com/p/obo-relations/wiki/ROAndTime" xsd:string property_value: IAO:0000116 "Parthood requires the part and the whole to have compatible classes: only an occurrent can be part of an occurrent; only a process can be part of a process; only a continuant can be part of a continuant; only an independent continuant can be part of an independent continuant; only an immaterial entity can be part of an immaterial entity; only a specifically dependent continuant can be part of a specifically dependent continuant; only a generically dependent continuant can be part of a generically dependent continuant. (This list is not exhaustive.)\n\nA continuant cannot be part of an occurrent: use 'participates in'. An occurrent cannot be part of a continuant: use 'has participant'. A material entity cannot be part of an immaterial entity: use 'has location'. A specifically dependent continuant cannot be part of an independent continuant: use 'inheres in'. An independent continuant cannot be part of a specifically dependent continuant: use 'bearer of'." xsd:string property_value: IAO:0000118 "part_of" xsd:string property_value: RO:0001900 RO:0001901 property_value: RO:0040042 BFO:0000002 property_value: RO:0040042 BFO:0000003 property_value: RO:0040042 BFO:0000004 property_value: RO:0040042 BFO:0000017 property_value: RO:0040042 BFO:0000019 property_value: RO:0040042 BFO:0000020 property_value: RO:0040042 BFO:0000031 property_value: seeAlso http://ontologydesignpatterns.org/wiki/Community:Parts_and_Collections property_value: seeAlso http://ontologydesignpatterns.org/wiki/Submissions:PartOf property_value: seeAlso "http://www.obofoundry.org/ro/#OBO_REL:part_of" xsd:string is_transitive: true is_a: RO:0002131 ! overlaps inverse_of: BFO:0000051 ! has part [Typedef] id: BFO:0000051 name: has part def: "a core relation that holds between a whole and its part" [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000111 "has part" xsd:string property_value: IAO:0000112 "my body has part my brain (continuant parthood, two material entities)" xsd:string property_value: IAO:0000112 "my stomach has part my stomach cavity (continuant parthood, material entity has part immaterial entity)" xsd:string property_value: IAO:0000112 "this year has part this day (occurrent parthood)" xsd:string property_value: IAO:0000116 "Everything has itself as a part. Any part of any part of a thing is itself part of that thing. Two distinct things cannot have each other as a part." xsd:string property_value: IAO:0000116 "Occurrents are not subject to change and so parthood between occurrents holds for all the times that the part exists. Many continuants are subject to change, so parthood between continuants will only hold at certain times, but this is difficult to specify in OWL. See http://purl.obolibrary.org/obo/ro/docs/temporal-semantics/" xsd:string property_value: IAO:0000116 "Occurrents are not subject to change and so parthood between occurrents holds for all the times that the part exists. Many continuants are subject to change, so parthood between continuants will only hold at certain times, but this is difficult to specify in OWL. See https://code.google.com/p/obo-relations/wiki/ROAndTime" xsd:string property_value: IAO:0000116 "Parthood requires the part and the whole to have compatible classes: only an occurrent have an occurrent as part; only a process can have a process as part; only a continuant can have a continuant as part; only an independent continuant can have an independent continuant as part; only a specifically dependent continuant can have a specifically dependent continuant as part; only a generically dependent continuant can have a generically dependent continuant as part. (This list is not exhaustive.)\n\nA continuant cannot have an occurrent as part: use 'participates in'. An occurrent cannot have a continuant as part: use 'has participant'. An immaterial entity cannot have a material entity as part: use 'location of'. An independent continuant cannot have a specifically dependent continuant as part: use 'bearer of'. A specifically dependent continuant cannot have an independent continuant as part: use 'inheres in'." xsd:string property_value: IAO:0000118 "has_part" xsd:string property_value: RO:0001900 RO:0001901 is_transitive: true is_a: RO:0002131 ! overlaps [Typedef] id: BFO:0000054 name: realized in comment: Paraphrase of elucidation: a relation between a realizable entity and a process, where there is some material entity that is bearer of the realizable entity and participates in the process, and the realizable entity comes to be realized in the course of the process property_value: IAO:0000111 "realized in" xsd:string property_value: IAO:0000112 "this disease is realized in this disease course" xsd:string property_value: IAO:0000112 "this fragility is realized in this shattering" xsd:string property_value: IAO:0000112 "this investigator role is realized in this investigation" xsd:string property_value: IAO:0000118 "is realized by" xsd:string property_value: IAO:0000118 "realized_in" xsd:string property_value: IAO:0000600 "[copied from inverse property 'realizes'] to say that b realizes c at t is to assert that there is some material entity d & b is a process which has participant d at t & c is a disposition or role of which d is bearer_of at t& the type instantiated by b is correlated with the type instantiated by c. (axiom label in BFO2 Reference: [059-003])" xsd:string property_value: isDefinedBy http://purl.obolibrary.org/obo/bfo.owl domain: BFO:0000017 ! realizable entity range: BFO:0000015 ! process inverse_of: BFO:0000055 ! realizes [Typedef] id: BFO:0000055 name: realizes comment: Paraphrase of elucidation: a relation between a process and a realizable entity, where there is some material entity that is bearer of the realizable entity and participates in the process, and the realizable entity comes to be realized in the course of the process property_value: IAO:0000111 "realizes" xsd:string property_value: IAO:0000112 "this disease course realizes this disease" xsd:string property_value: IAO:0000112 "this investigation realizes this investigator role" xsd:string property_value: IAO:0000112 "this shattering realizes this fragility" xsd:string property_value: IAO:0000600 "to say that b realizes c at t is to assert that there is some material entity d & b is a process which has participant d at t & c is a disposition or role of which d is bearer_of at t& the type instantiated by b is correlated with the type instantiated by c. (axiom label in BFO2 Reference: [059-003])" xsd:string property_value: isDefinedBy http://purl.obolibrary.org/obo/iao.owl domain: BFO:0000015 ! process range: BFO:0000017 ! realizable entity [Typedef] id: BFO:0000062 name: preceded by def: "x is preceded by y if and only if the time point at which y ends is before or equivalent to the time point at which x starts. Formally: x preceded by y iff ω(y) <= α(x), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point." [] subset: ro-eco property_value: http://purl.org/dc/elements/1.1/source "http://www.obofoundry.org/ro/#OBO_REL:preceded_by" xsd:string property_value: IAO:0000111 "preceded by" xsd:string property_value: IAO:0000116 "An example is: translation preceded_by transcription; aging preceded_by development (not however death preceded_by aging). Where derives_from links classes of continuants, preceded_by links classes of processes. Clearly, however, these two relations are not independent of each other. Thus if cells of type C1 derive_from cells of type C, then any cell division involving an instance of C1 in a given lineage is preceded_by cellular processes involving an instance of C. The assertion P preceded_by P1 tells us something about Ps in general: that is, it tells us something about what happened earlier, given what we know about what happened later. Thus it does not provide information pointing in the opposite direction, concerning instances of P1 in general; that is, that each is such as to be succeeded by some instance of P. Note that an assertion to the effect that P preceded_by P1 is rather weak; it tells us little about the relations between the underlying instances in virtue of which the preceded_by relation obtains. Typically we will be interested in stronger relations, for example in the relation immediately_preceded_by, or in relations which combine preceded_by with a condition to the effect that the corresponding instances of P and P1 share participants, or that their participants are connected by relations of derivation, or (as a first step along the road to a treatment of causality) that the one process in some way affects (for example, initiates or regulates) the other." xsd:string property_value: IAO:0000118 "is preceded by" xsd:string property_value: IAO:0000118 "preceded_by" xsd:string domain: BFO:0000003 ! occurrent range: BFO:0000003 ! occurrent holds_over_chain: BFO:0000050 BFO:0000062 is_transitive: true is_a: RO:0002086 ! ends after inverse_of: BFO:0000063 ! precedes [Typedef] id: BFO:0000063 name: precedes def: "x precedes y if and only if the time point at which x ends is before or equivalent to the time point at which y starts. Formally: x precedes y iff ω(x) <= α(y), where α is a function that maps a process to a start point, and ω is a function that maps a process to an end point." [] subset: ro-eco property_value: IAO:0000111 "precedes" xsd:string domain: BFO:0000003 ! occurrent range: BFO:0000003 ! occurrent holds_over_chain: BFO:0000050 BFO:0000063 is_transitive: true is_a: RO:0002222 ! temporally related to [Typedef] id: IAO:0000136 name: is about def: "'is about' relates an information entity to other entities in which the information entity holds some information which dscribes some facet of the other entity, such as the arrow direction on a sign." [] def: "A (currently) primitive relation that relates an information artifact to an entity." [] property_value: http://purl.org/dc/terms/creator "Alan Ruttenberg" xsd:string property_value: IAO:0000112 "This document is about information artifacts and their representations" xsd:string property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "7/6/2009 Alan Ruttenberg. Following discussion with Jonathan Rees, and introduction of \"mentions\" relation. Weaken the is_about relationship to be primitive. \n\nWe will try to build it back up by elaborating the various subproperties that are more precisely defined.\n\nSome currently missing phenomena that should be considered \"about\" are predications - \"The only person who knows the answer is sitting beside me\" , Allegory, Satire, and other literary forms that can be topical without explicitly mentioning the topic." xsd:string property_value: IAO:0000117 "person:Alan Ruttenberg" xsd:string property_value: IAO:0000119 "IAO" xsd:string property_value: IAO:0000119 "James Malone" xsd:string property_value: IAO:0000119 "Smith, Ceusters, Ruttenberg, 2000 years of philosophy" xsd:string domain: IAO:0000030 ! information content entity domain: IAO:0000030 ! information content entity [Typedef] id: OBI:0000293 name: has_specified_input def: "The inverse property of is_specified_input_of" [] property_value: IAO:0000111 "has_specified_input" xsd:string property_value: IAO:0000111 "has_specified_input" xsd:string property_value: IAO:0000112 "see is_input_of example_of_usage" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000116 "8/17/09: specified inputs of one process are not necessarily specified inputs of a larger process that it is part of. This is in contrast to how 'has participant' works." xsd:string property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Larry Hunter" xsd:string property_value: IAO:0000117 "PERSON: Melanie Coutot" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/obi.owl domain: OBI:0000011 ! planned process is_a: RO:0000057 ! has participant inverse_of: OBI:0000295 ! is_specified_input_of [Typedef] id: OBI:0000295 name: is_specified_input_of def: "A relation between a planned process and a continuant participating in that process that is not created during the process. The presence of the continuant during the process is explicitly specified in the plan specification which the process realizes the concretization of." [] property_value: IAO:0000111 "is_specified_input_of" xsd:string property_value: IAO:0000112 "some Autologous EBV(Epstein-Barr virus)-transformed B-LCL (B lymphocyte cell line) is_input_for instance of Chromum Release Assay described at https://wiki.cbil.upenn.edu/obiwiki/index.php/Chromium_Release_assay" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON:Bjoern Peters" xsd:string range: OBI:0000011 ! planned process is_a: RO:0000056 ! participates in [Typedef] id: OBI:0000299 name: has_specified_output def: "The inverse property of is_specified_output_of" [] property_value: IAO:0000111 "has_specified_output" xsd:string property_value: IAO:0000111 "has_specified_output" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "PERSON: Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON: Bjoern Peters" xsd:string property_value: IAO:0000117 "PERSON: Larry Hunter" xsd:string property_value: IAO:0000117 "PERSON: Melanie Courtot" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/obi.owl domain: OBI:0000011 ! planned process is_a: RO:0000057 ! has participant inverse_of: OBI:0000312 ! is_specified_output_of [Typedef] id: OBI:0000312 name: is_specified_output_of def: "A relation between a planned process and a continuant participating in that process. The presence of the continuant at the end of the process is explicitly specified in the objective specification which the process realizes the concretization of." [] property_value: IAO:0000111 "is_specified_output_of" xsd:string property_value: IAO:0000111 "is_specified_output_of" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "Alan Ruttenberg" xsd:string property_value: IAO:0000117 "PERSON:Bjoern Peters" xsd:string property_value: IAO:0000412 http://purl.obolibrary.org/obo/obi.owl range: OBI:0000011 ! planned process is_a: RO:0000056 ! participates in [Typedef] id: OBI:0000417 name: achieves_planned_objective def: "This relation obtains between a planned process and a objective specification when the criteria specified in the objective specification are met at the end of the planned process." [] property_value: IAO:0000111 "achieves_planned_objective" xsd:string property_value: IAO:0000112 "A cell sorting process achieves the objective specification 'material separation objective'" xsd:string property_value: IAO:0000114 IAO:0000120 property_value: IAO:0000117 "BP, AR, PPPB branch" xsd:string property_value: IAO:0000119 "PPPB branch derived" xsd:string property_value: IAO:0000232 "modified according to email thread from 1/23/09 in accordince with DT and PPPB branch" xsd:string domain: OBI:0000011 ! planned process range: IAO:0000005 ! objective specification inverse_of: OBI:0000833 ! objective_achieved_by [Typedef] id: OBI:0000833 name: objective_achieved_by def: "This relation obtains between an objective specification and a planned process when the criteria specified in the objective specification are met at the end of the planned process." [] property_value: IAO:0000111 "objective_achieved_by" xsd:string property_value: IAO:0000114 IAO:0000122 property_value: IAO:0000117 "OBI" xsd:string property_value: IAO:0000119 "OBI" xsd:string domain: IAO:0000005 ! objective specification range: OBI:0000011 ! planned process [Typedef] id: RO:0000052 name: characteristic of name: inheres in def: "a relation between a specifically dependent continuant (the characteristic) and any other entity (the bearer), in which the characteristic depends on the bearer for its existence." [] def: "a relation between a specifically dependent continuant (the dependent) and an independent continuant (the bearer), in which the dependent specifically depends on the bearer for its existence" [] comment: Note that this relation was previously called "inheres in", but was changed to be called "characteristic of" because BFO2 uses "inheres in" in a more restricted fashion. This relation differs from BFO2:inheres_in in two respects: (1) it does not impose a range constraint, and thus it allows qualities of processes, as well as of information entities, whereas BFO2 restricts inheres_in to only apply to independent continuants (2) it is declared functional, i.e. something can only be a characteristic of one thing. property_value: IAO:0000111 "inheres in" xsd:string property_value: IAO:0000112 "this fragility inheres in this vase" xsd:string property_value: IAO:0000112 "this fragility is a characteristic of this vase" xsd:string property_value: IAO:0000112 "this red color inheres in this apple" xsd:string property_value: IAO:0000112 "this red color is a characteristic of this apple" xsd:string property_value: IAO:0000116 "A dependent inheres in its bearer at all times for which the dependent exists." xsd:string property_value: IAO:0000118 "inheres_in" xsd:string property_value: RO:0001900 RO:0001901 is_functional: true is_a: RO:0002314 ! characteristic of part of inverse_of: RO:0000053 ! bearer of [Typedef] id: RO:0000053 name: bearer of name: has characteristic def: "a relation between an independent continuant (the bearer) and a specifically dependent continuant (the dependent), in which the dependent specifically depends on the bearer for its existence" [] def: "Inverse of characteristic_of" [] property_value: IAO:0000111 "bearer of" xsd:string property_value: IAO:0000112 "this apple is bearer of this red color" xsd:string property_value: IAO:0000112 "this vase is bearer of this fragility" xsd:string property_value: IAO:0000116 "A bearer can have many dependents, and its dependents can exist for different periods of time, but none of its dependents can exist when the bearer does not exist." xsd:string property_value: IAO:0000118 "bearer_of" xsd:string property_value: IAO:0000118 "is bearer of" xsd:string property_value: RO:0001900 RO:0001901 range: BFO:0000020 ! specifically dependent continuant is_inverse_functional: true [Typedef] id: RO:0000056 name: participates in def: "a relation between a continuant and a process, in which the continuant is somehow involved in the process" [] comment: Please see the official RO definition for the inverse of this property, 'has participant.' property_value: http://purl.org/dc/terms/creator "Andy Brown" xsd:string property_value: IAO:0000111 "participates in" xsd:string property_value: IAO:0000112 "this blood clot participates in this blood coagulation" xsd:string property_value: IAO:0000112 "this input material (or this output material) participates in this process" xsd:string property_value: IAO:0000112 "this investigator participates in this investigation" xsd:string property_value: IAO:0000118 "participates_in" xsd:string domain: BFO:0000002 ! continuant range: BFO:0000003 ! occurrent inverse_of: RO:0000057 ! has participant [Typedef] id: RO:0000057 name: has participant def: "a relation between a process and a continuant, in which the continuant is somehow involved in the process" [] def: "Has_participant is a primitive instance-level relation between a process, a continuant, and a time at which the continuant participates in some way in the process." [] property_value: http://purl.org/dc/elements/1.1/source "http://www.obofoundry.org/ro/#OBO_REL:has_participant" xsd:string property_value: http://purl.org/dc/terms/creator "Andy Brown" xsd:string property_value: IAO:0000111 "has participant" xsd:string property_value: IAO:0000112 "The relation obtains, for example, when this particular process of oxygen exchange across this particular alveolar membrane has_participant this particular sample of hemoglobin at this particular time." xsd:string property_value: IAO:0000112 "this blood coagulation has participant this blood clot" xsd:string property_value: IAO:0000112 "this investigation has participant this investigator" xsd:string property_value: IAO:0000112 "this process has participant this input material (or this output material)" xsd:string property_value: IAO:0000116 "Has_participant is a primitive instance-level relation between a process, a continuant, and a time at which the continuant participates in some way in the process. The relation obtains, for example, when this particular process of oxygen exchange across this particular alveolar membrane has_participant this particular sample of hemoglobin at this particular time." xsd:string property_value: IAO:0000118 "has_participant" xsd:string property_value: IAO:0000119 "http://obo-relations.googlecode.com/svn/trunk/src/ontology/core.owl" xsd:string domain: BFO:0000003 ! occurrent range: BFO:0000002 ! continuant holds_over_chain: BFO:0000051 RO:0000057 [Typedef] id: RO:0000058 name: is concretized as def: "A relationship between a generically dependent continuant and a specifically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. A generically dependent continuant may be concretized as multiple specifically dependent continuants." [] property_value: IAO:0000112 "A journal article is an information artifact that inheres in some number of printed journals. For each copy of the printed journal there is some quality that carries the journal article, such as a pattern of ink. The journal article (a generically dependent continuant) is concretized as the quality (a specifically dependent continuant), and both depend on that copy of the printed journal (an independent continuant)." xsd:string property_value: IAO:0000112 "An investigator reads a protocol and forms a plan to carry out an assay. The plan is a realizable entity (a specifically dependent continuant) that concretizes the protocol (a generically dependent continuant), and both depend on the investigator (an independent continuant). The plan is then realized by the assay (a process)." xsd:string domain: BFO:0000031 ! generically dependent continuant range: BFO:0000020 ! specifically dependent continuant inverse_of: RO:0000059 ! concretizes [Typedef] id: RO:0000059 name: concretizes def: "A relationship between a specifically dependent continuant and a generically dependent continuant, in which the generically dependent continuant depends on some independent continuant in virtue of the fact that the specifically dependent continuant also depends on that same independent continuant. Multiple specifically dependent continuants can concretize the same generically dependent continuant." [] property_value: IAO:0000112 "A journal article is an information artifact that inheres in some number of printed journals. For each copy of the printed journal there is some quality that carries the journal article, such as a pattern of ink. The quality (a specifically dependent continuant) concretizes the journal article (a generically dependent continuant), and both depend on that copy of the printed journal (an independent continuant)." xsd:string property_value: IAO:0000112 "An investigator reads a protocol and forms a plan to carry out an assay. The plan is a realizable entity (a specifically dependent continuant) that concretizes the protocol (a generically dependent continuant), and both depend on the investigator (an independent continuant). The plan is then realized by the assay (a process)." xsd:string domain: BFO:0000020 ! specifically dependent continuant range: BFO:0000031 ! generically dependent continuant [Typedef] id: RO:0000079 name: function of def: "a relation between a function and an independent continuant (the bearer), in which the function specifically depends on the bearer for its existence" [] comment: This relation is modeled after the BFO relation of the same name which was in BFO2, but is used in a more restricted sense - specifically, we model this relation as functional (inherited from characteristic-of). Note that this relation is now removed from BFO2020. property_value: IAO:0000112 "this catalysis function is a function of this enzyme" xsd:string property_value: IAO:0000116 "A function inheres in its bearer at all times for which the function exists, however the function need not be realized at all the times that the function exists." xsd:string property_value: IAO:0000118 "function_of" xsd:string property_value: IAO:0000118 "is function of" xsd:string domain: BFO:0000034 ! function is_a: RO:0000052 ! inheres in inverse_of: RO:0000085 ! has function [Typedef] id: RO:0000080 name: quality of def: "a relation between a quality and an independent continuant (the bearer), in which the quality specifically depends on the bearer for its existence" [] comment: This relation is modeled after the BFO relation of the same name which was in BFO2, but is used in a more restricted sense - specifically, we model this relation as functional (inherited from characteristic-of). Note that this relation is now removed from BFO2020. property_value: IAO:0000112 "this red color is a quality of this apple" xsd:string property_value: IAO:0000116 "A quality inheres in its bearer at all times for which the quality exists." xsd:string property_value: IAO:0000118 "is quality of" xsd:string property_value: IAO:0000118 "quality_of" xsd:string is_a: RO:0000052 ! inheres in inverse_of: RO:0000086 ! has quality [Typedef] id: RO:0000081 name: role of def: "a relation between a role and an independent continuant (the bearer), in which the role specifically depends on the bearer for its existence" [] comment: This relation is modeled after the BFO relation of the same name which was in BFO2, but is used in a more restricted sense - specifically, we model this relation as functional (inherited from characteristic-of). Note that this relation is now removed from BFO2020. property_value: IAO:0000112 "this investigator role is a role of this person" xsd:string property_value: IAO:0000116 "A role inheres in its bearer at all times for which the role exists, however the role need not be realized at all the times that the role exists." xsd:string property_value: IAO:0000118 "is role of" xsd:string property_value: IAO:0000118 "role_of" xsd:string is_a: RO:0000052 ! inheres in inverse_of: RO:0000087 ! has role [Typedef] id: RO:0000085 name: has function def: "a relation between an independent continuant (the bearer) and a function, in which the function specifically depends on the bearer for its existence" [] property_value: IAO:0000112 "this enzyme has function this catalysis function (more colloquially: this enzyme has this catalysis function)" xsd:string property_value: IAO:0000116 "A bearer can have many functions, and its functions can exist for different periods of time, but none of its functions can exist when the bearer does not exist. A function need not be realized at all the times that the function exists." xsd:string property_value: IAO:0000118 "has_function" xsd:string domain: BFO:0000004 ! independent continuant range: BFO:0000034 ! function is_a: RO:0000053 ! bearer of [Typedef] id: RO:0000086 name: has quality def: "a relation between an independent continuant (the bearer) and a quality, in which the quality specifically depends on the bearer for its existence" [] property_value: IAO:0000112 "this apple has quality this red color" xsd:string property_value: IAO:0000116 "A bearer can have many qualities, and its qualities can exist for different periods of time, but none of its qualities can exist when the bearer does not exist." xsd:string property_value: IAO:0000118 "has_quality" xsd:string range: BFO:0000019 ! quality is_a: RO:0000053 ! bearer of [Typedef] id: RO:0000087 name: has role name: has_role def: "a relation between an independent continuant (the bearer) and a role, in which the role specifically depends on the bearer for its existence" [] property_value: IAO:0000112 "this person has role this investigator role (more colloquially: this person has this role of investigator)" xsd:string property_value: IAO:0000116 "A bearer can have many roles, and its roles can exist for different periods of time, but none of its roles can exist when the bearer does not exist. A role need not be realized at all the times that the role exists." xsd:string property_value: IAO:0000118 "has_role" xsd:string domain: BFO:0000004 ! independent continuant range: BFO:0000023 ! role is_a: RO:0000053 ! bearer of [Typedef] id: RO:0000091 name: has disposition def: "a relation between an independent continuant (the bearer) and a disposition, in which the disposition specifically depends on the bearer for its existence" [] domain: BFO:0000004 ! independent continuant range: BFO:0000016 ! disposition is_a: RO:0000053 ! bearer of inverse_of: RO:0000092 ! disposition of [Typedef] id: RO:0000092 name: disposition of def: "inverse of has disposition" [] comment: This relation is modeled after the BFO relation of the same name which was in BFO2, but is used in a more restricted sense - specifically, we model this relation as functional (inherited from characteristic-of). Note that this relation is now removed from BFO2020. subset: RO:0002259 is_a: RO:0000052 ! inheres in [Typedef] id: RO:0002013 name: has regulatory component activity def: "A 'has regulatory component activity' B if A and B are GO molecular functions (GO_0003674), A has_component B and A is regulated by B." [] is_a: RO:0002017 ! has component activity is_a: RO:0002334 ! regulated by created_by: dos creation_date: 2017-05-24T09:30:46Z [Typedef] id: RO:0002014 name: has negative regulatory component activity def: "A relationship that holds between a GO molecular function and a component of that molecular function that negatively regulates the activity of the whole. More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is negatively regulated by B." [] comment: By convention GO molecular functions are classified by their effector function. Internal regulatory functions are treated as components. For example, NMDA glutmate receptor activity is a cation channel activity with positive regulatory component 'glutamate binding' and negative regulatory components including 'zinc binding' and 'magnesium binding'. is_a: RO:0002013 ! has regulatory component activity is_a: RO:0002335 ! negatively regulated by created_by: dos creation_date: 2017-05-24T09:31:01Z [Typedef] id: RO:0002015 name: has positive regulatory component activity def: "A relationship that holds between a GO molecular function and a component of that molecular function that positively regulates the activity of the whole. More formally, A 'has regulatory component activity' B iff :A and B are GO molecular functions (GO_0003674), A has_component B and A is positively regulated by B." [] comment: By convention GO molecular functions are classified by their effector function and internal regulatory functions are treated as components. So, for example calmodulin has a protein binding activity that has positive regulatory component activity calcium binding activity. Receptor tyrosine kinase activity is a tyrosine kinase activity that has positive regulatory component 'ligand binding'. is_a: RO:0002013 ! has regulatory component activity is_a: RO:0002336 ! positively regulated by created_by: dos creation_date: 2017-05-24T09:31:17Z [Typedef] id: RO:0002017 name: has component activity comment: A 'has component activity' B if A is A and B are molecular functions (GO_0003674) and A has_component B. is_a: RO:0002018 ! has component process created_by: dos creation_date: 2017-05-24T09:44:33Z [Typedef] id: RO:0002018 name: has component process def: "w 'has process component' p if p and w are processes, w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type." [] domain: BFO:0000015 ! process range: BFO:0000015 ! process is_a: RO:0002180 ! has component created_by: dos creation_date: 2017-05-24T09:49:21Z [Typedef] id: RO:0002022 name: directly regulated by comment: Process(P2) is directly regulated by process(P1) iff: P1 regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2). For example, if protein A has protein binding activity(P1) that targets protein B and this binding regulates the kinase activity (P2) of protein B then P1 directly regulates P2. {type="owl:Axiom", xref="GOC:dos"} is_a: RO:0002334 ! regulated by inverse_of: RO:0002578 ! directly regulates created_by: dos creation_date: 2017-09-17T13:52:24Z [Typedef] id: RO:0002023 name: directly negatively regulated by def: "Process(P2) is directly negatively regulated by process(P1) iff: P1 negatively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2). For example, if protein A has protein binding activity(P1) that targets protein B and this binding negatively regulates the kinase activity (P2) of protein B then P2 directly negatively regulated by P1." [GOC:dos] {type="owl:Axiom"} is_a: RO:0002022 ! directly regulated by inverse_of: RO:0002630 ! directly negatively regulates created_by: dos creation_date: 2017-09-17T13:52:38Z [Typedef] id: RO:0002024 name: directly positively regulated by def: "Process(P2) is directly postively regulated by process(P1) iff: P1 positively regulates P2 via direct physical interaction between an agent executing P1 (or some part of P1) and an agent executing P2 (or some part of P2). For example, if protein A has protein binding activity(P1) that targets protein B and this binding positively regulates the kinase activity (P2) of protein B then P2 is directly postively regulated by P1." [GOC:dos] {type="owl:Axiom"} is_a: RO:0002022 ! directly regulated by inverse_of: RO:0002629 ! directly positively regulates created_by: dos creation_date: 2017-09-17T13:52:47Z [Typedef] id: RO:0002025 name: has effector activity def: "A 'has effector activity' B if A and B are GO molecular functions (GO_0003674), A 'has component activity' B and B is the effector (output function) of B. Each compound function has only one effector activity." [GOC:dos] {type="owl:Axiom"} comment: This relation is designed for constructing compound molecular functions, typically in combination with one or more regulatory component activity relations. is_functional: true is_a: RO:0002017 ! has component activity created_by: dos creation_date: 2017-09-22T14:14:36Z [Typedef] id: RO:0002086 name: ends after comment: X ends_after Y iff: end(Y) before_or_simultaneous_with end(X) subset: ro-eco property_value: IAO:0000117 "David Osumi-Sutherland" xsd:string is_transitive: true is_a: RO:0002222 ! temporally related to [Typedef] id: RO:0002087 name: immediately preceded by comment: X immediately_preceded_by Y iff: end(X) simultaneous_with start(Y) property_value: IAO:0000117 "David Osumi-Sutherland" xsd:string property_value: IAO:0000118 "starts_at_end_of" xsd:string is_a: BFO:0000062 ! preceded by inverse_of: RO:0002090 ! immediately precedes [Typedef] id: RO:0002090 name: immediately precedes comment: X immediately_precedes_Y iff: end(X) simultaneous_with start(Y) subset: ro-eco property_value: IAO:0000117 "David Osumi-Sutherland" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-7073-9172 property_value: IAO:0000118 "ends_at_start_of" xsd:string property_value: IAO:0000118 "meets" xsd:string property_value: RO:0002575 BFO:0000063 is_a: BFO:0000063 ! precedes [Typedef] id: RO:0002131 name: overlaps def: "x overlaps y if and only if there exists some z such that x has part z and z part of y" [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_gocam subset: ro-eco property_value: IAO:0000114 IAO:0000125 property_value: RO:0001900 RO:0001901 holds_over_chain: BFO:0000050 BFO:0000050 holds_over_chain: BFO:0000051 BFO:0000050 {RO:0002582="true", type="owl:Axiom"} holds_over_chain: BFO:0000051 RO:0002131 is_symmetric: true is_a: RO:0002323 ! mereotopologically related to transitive_over: BFO:0000050 ! part of expand_expression_to: "http://purl.obolibrary.org/obo/BFO_0000051 some (http://purl.obolibrary.org/obo/BFO_0000050 some ?Y)" [] [Typedef] id: RO:0002180 name: has component def: "w 'has component' p if w 'has part' p and w is such that it can be directly disassembled into into n parts p, p2, p3, ..., pn, where these parts are of similar type." [] subset: ro-eco property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000116 "The definition of 'has component' is still under discussion. The challenge is in providing a definition that does not imply transitivity." xsd:string property_value: IAO:0000232 "For use in recording has_part with a cardinality constraint, because OWL does not permit cardinality constraints to be used in combination with transitive object properties. In situations where you would want to say something like 'has part exactly 5 digit, you would instead use has_component exactly 5 digit." xsd:string property_value: RO:0001900 RO:0001901 property_value: seeAlso http://ontologydesignpatterns.org/wiki/Submissions:Componency is_a: BFO:0000051 ! has part [Typedef] id: RO:0002211 name: regulates def: "p regulates q iff p is causally upstream of q, the execution of p is not constant and varies according to specific conditions, and p influences the rate or magnitude of execution of q due to an effect either on some enabler of q or some enabler of a part of q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0001-7476-6306 property_value: IAO:0000117 https://orcid.org/0000-0002-3837-8864 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 "GO" xsd:string property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000232 "Regulation precludes parthood; the regulatory process may not be within the regulated process." xsd:string property_value: IAO:0000589 "regulates (processual)" xsd:string property_value: IAO:0000600 "false" xsd:boolean domain: BFO:0000015 ! process range: BFO:0000015 ! process holds_over_chain: RO:0002578 RO:0002578 is_transitive: true is_a: RO:0002411 ! causally upstream of inverse_of: RO:0002334 ! regulated by transitive_over: RO:0002025 ! has effector activity [Typedef] id: RO:0002212 name: negatively regulates def: "p negatively regulates q iff p regulates q, and p decreases the rate or magnitude of execution of q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "negatively regulates (process to process)" xsd:string property_value: RO:0004050 RO:0002211 is_a: RO:0002211 ! regulates is_a: RO:0002305 ! causally upstream of, negative effect inverse_of: RO:0002335 ! negatively regulated by [Typedef] id: RO:0002213 name: positively regulates def: "p positively regulates q iff p regulates q, and p increases the rate or magnitude of execution of q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "positively regulates (process to process)" xsd:string property_value: RO:0004049 RO:0002211 holds_over_chain: RO:0002212 RO:0002212 is_transitive: true is_a: RO:0002211 ! regulates is_a: RO:0002304 ! causally upstream of, positive effect inverse_of: RO:0002336 ! positively regulated by [Typedef] id: RO:0002215 name: capable of def: "A relation between a material entity (such as a cell) and a process, in which the material entity has the ability to carry out the process. " [] subset: ro-eco property_value: IAO:0000112 "mechanosensory neuron capable of detection of mechanical stimulus involved in sensory perception (GO:0050974)" xsd:string property_value: IAO:0000112 "osteoclast SubClassOf 'capable of' some 'bone resorption'" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "has function realized in" xsd:string property_value: IAO:0000119 http://www.ncbi.nlm.nih.gov/pubmed/20123131 property_value: IAO:0000119 http://www.ncbi.nlm.nih.gov/pubmed/21208450 property_value: IAO:0000232 "For compatibility with BFO, this relation has a shortcut definition in which the expression \"capable of some P\" expands to \"bearer_of (some realized_by only P)\"." xsd:string domain: BFO:0000004 ! independent continuant range: BFO:0000015 ! process is_a: RO:0002216 ! capable of part of [Typedef] id: RO:0002216 name: capable of part of def: "c stands in this relationship to p if and only if there exists some p' such that c is capable_of p', and p' is part_of p." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "has function in" xsd:string property_value: seeAlso http://purl.obolibrary.org/obo/ro/docs/reflexivity/ holds_over_chain: RO:0002215 BFO:0000050 {RO:0002582="true", type="owl:Axiom"} is_a: RO:0002328 ! functionally related to is_a: RO:0002500 ! causal agent in process [Typedef] id: RO:0002222 name: temporally related to comment: A relation that holds between two occurrents. This is a grouping relation that collects together all the Allen relations. subset: ro-eco property_value: http://purl.org/dc/terms/source "https://docs.google.com/document/d/1kBv1ep_9g3sTR-SD3jqzFqhuwo9TPNF-l-9fUDbO6rM/edit?pli=1" xsd:anyURI property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 https://en.wikipedia.org/wiki/Allen%27s_interval_algebra property_value: IAO:0000232 "Do not use this relation directly. It is ended as a grouping for relations between occurrents involving the relative timing of their starts and ends." xsd:string domain: BFO:0000003 ! occurrent range: BFO:0000003 ! occurrent [Typedef] id: RO:0002233 name: has input def: "p has input c iff: p is a process, c is a material entity, c is a participant in p, c is present at the start of p, and the state of c is modified during p." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam subset: ro-eco property_value: IAO:0000114 IAO:0000125 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "consumes" xsd:string domain: BFO:0000015 ! process is_a: RO:0000057 ! has participant inverse_of: RO:0002352 ! input of [Typedef] id: RO:0002263 name: acts upstream of def: "c acts upstream of p if and only if c enables some f that is involved in p' and p' occurs chronologically before p, is not part of p, and affects the execution of p. c is a material entity and f, p, p' are processes." [] subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: IAO:0000112 "A faulty traffic light (material entity) whose malfunctioning (a process) is causally upstream of a traffic collision (a process): the traffic light acts upstream of the collision." xsd:string property_value: seeAlso http://wiki.geneontology.org/index.php/Acts_upstream_of holds_over_chain: RO:0002327 RO:0002411 is_a: RO:0002264 ! acts upstream of or within [Typedef] id: RO:0002264 name: acts upstream of or within def: "c acts upstream of or within p if c is enables f, and f is causally upstream of or within p. c is a material entity and p is an process." [] subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term synonym: "affects" RELATED [] property_value: IAO:0000112 "A gene product that has some activity, where that activity may be a part of a pathway or upstream of the pathway." xsd:string property_value: seeAlso http://wiki.geneontology.org/index.php/Acts_upstream_of_or_within holds_over_chain: RO:0002327 RO:0002418 is_a: RO:0002500 ! causal agent in process [Typedef] id: RO:0002304 name: causally upstream of, positive effect def: "p is causally upstream of, positive effect q iff p is casually upstream of q, and the execution of p is required for the execution of q." [] comment: holds between x and y if and only if x is causally upstream of y and the progression of x increases the frequency, rate or extent of y subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: http://purl.org/dc/terms/creator https://orcid.org/0000-0002-6601-2165 property_value: RO:0004049 RO:0002411 is_a: RO:0002411 ! causally upstream of is_a: RO:0004047 ! causally upstream of or within, positive effect [Typedef] id: RO:0002305 name: causally upstream of, negative effect def: "p is causally upstream of, negative effect q iff p is casually upstream of q, and the execution of p decreases the execution of q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: http://purl.org/dc/terms/creator https://orcid.org/0000-0002-6601-2165 property_value: RO:0004050 RO:0002411 is_a: RO:0002411 ! causally upstream of is_a: RO:0004046 ! causally upstream of or within, negative effect [Typedef] id: RO:0002314 name: characteristic of part of def: "q characteristic of part of w if and only if there exists some p such that q inheres in p and p part of w." [] property_value: IAO:0000116 "Because part_of is transitive, inheres in is a sub-relation of characteristic of part of" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "inheres in part of" xsd:string property_value: IAO:0000119 http://www.ncbi.nlm.nih.gov/pubmed/20064205 property_value: RO:0001900 RO:0001901 property_value: seeAlso http://purl.obolibrary.org/obo/ro/docs/reflexivity/ holds_over_chain: RO:0000052 BFO:0000050 {RO:0002582="true", type="owl:Axiom"} is_a: RO:0002502 ! depends on transitive_over: BFO:0000050 ! part of [Typedef] id: RO:0002323 name: mereotopologically related to def: "A mereological relationship or a topological relationship" [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000232 "Do not use this relation directly. It is ended as a grouping for a diverse set of relations, all involving parthood or connectivity relationships" xsd:string property_value: RO:0001900 RO:0001901 [Typedef] id: RO:0002327 name: enables def: "c enables p iff c is capable of p and c acts to execute p." [] subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: IAO:0000112 "a particular instances of akt-2 enables some instance of protein kinase activity" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "catalyzes" xsd:string property_value: IAO:0000118 "executes" xsd:string property_value: IAO:0000118 "has" xsd:string property_value: IAO:0000118 "is catalyzing" xsd:string property_value: IAO:0000118 "is executing" xsd:string property_value: IAO:0000232 "This relation differs from the parent relation 'capable of' in that the parent is weaker and only expresses a capability that may not be actually realized, whereas this relation is always realized." xsd:string is_a: RO:0002215 ! capable of inverse_of: RO:0002333 ! enabled by transitive_over: BFO:0000051 ! has part transitive_over: RO:0002017 ! has component activity [Typedef] id: RO:0002328 name: functionally related to def: "A grouping relationship for any relationship directly involving a function, or that holds because of a function of one of the related entities." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000232 "This is a grouping relation that collects relations used for the purpose of connecting structure and function" xsd:string [Typedef] id: RO:0002329 name: part of structure that is capable of def: "this relation holds between c and p when c is part of some c', and c' is capable of p." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "false" xsd:boolean holds_over_chain: BFO:0000050 RO:0002215 {RO:0002581="true", type="owl:Axiom"} is_a: RO:0002328 ! functionally related to [Typedef] id: RO:0002331 name: involved in def: "c involved_in p if and only if c enables some process p', and p' is part of p" [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "actively involved in" xsd:string property_value: IAO:0000118 "enables part of" xsd:string property_value: seeAlso Involved:in holds_over_chain: RO:0002327 BFO:0000050 is_a: RO:0000056 ! participates in is_a: RO:0002431 ! involved in or involved in regulation of transitive_over: BFO:0000050 ! part of [Typedef] id: RO:0002333 name: enabled by def: "inverse of enables" [] subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0000057 ! has participant is_a: RO:0002328 ! functionally related to [Typedef] id: RO:0002334 name: regulated by def: "inverse of regulates" [] subset: RO:0002259 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000589 "regulated by (processual)" xsd:string domain: BFO:0000015 ! process range: BFO:0000015 ! process is_transitive: true is_a: RO:0002427 ! causally downstream of or within [Typedef] id: RO:0002335 name: negatively regulated by def: "inverse of negatively regulates" [] subset: RO:0002259 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0002334 ! regulated by [Typedef] id: RO:0002336 name: positively regulated by def: "inverse of positively regulates" [] subset: RO:0002259 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0002334 ! regulated by [Typedef] id: RO:0002350 name: member of def: "is member of is a mereological relation between a item and a collection." [] property_value: IAO:0000112 "An organism that is a member of a population of organisms" xsd:string property_value: IAO:0000118 "is member of" xsd:string property_value: IAO:0000118 "member part of" xsd:string property_value: IAO:0000119 "SIO" xsd:string property_value: RO:0001900 RO:0001901 is_a: BFO:0000050 ! part of inverse_of: RO:0002351 ! has member [Typedef] id: RO:0002351 name: has member def: "has member is a mereological relation between a collection and an item." [] property_value: IAO:0000119 "SIO" xsd:string property_value: RO:0001900 RO:0001901 is_a: BFO:0000051 ! has part [Typedef] id: RO:0002352 name: input of def: "An entity A is the 'input of' another entity B if A was put into the system, entity or software represented by B." [] def: "inverse of has input" [] subset: ro-eco subset: RO:0002259 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 "Allyson Lister" xsd:string is_a: RO:0000056 ! participates in is_a: RO:0002328 ! functionally related to [Typedef] id: RO:0002404 name: causally downstream of def: "inverse of upstream of" [] property_value: IAO:0000114 IAO:0000428 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: BFO:0000062 ! preceded by is_a: RO:0002427 ! causally downstream of or within inverse_of: RO:0002411 ! causally upstream of [Typedef] id: RO:0002405 name: immediately causally downstream of property_value: IAO:0000114 IAO:0000428 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0002087 ! immediately preceded by is_a: RO:0002404 ! causally downstream of inverse_of: RO:0002412 ! immediately causally upstream of [Typedef] id: RO:0002407 name: indirectly positively regulates def: "p indirectly positively regulates q iff p is indirectly causally upstream of q and p positively regulates q." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "indirectly activates" xsd:string property_value: RO:0002579 RO:0002213 holds_over_chain: RO:0002409 RO:0002409 holds_over_chain: RO:0002629 RO:0002407 holds_over_chain: RO:0002629 RO:0002629 is_transitive: true is_a: RO:0002213 ! positively regulates is_a: RO:0012012 ! indirectly regulates transitive_over: RO:0002629 ! directly positively regulates [Typedef] id: RO:0002409 name: indirectly negatively regulates def: "p indirectly negatively regulates q iff p is indirectly causally upstream of q and p negatively regulates q." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "indirectly inhibits" xsd:string property_value: RO:0002579 RO:0002212 holds_over_chain: RO:0002630 RO:0002409 holds_over_chain: RO:0002630 RO:0002630 is_transitive: true is_a: RO:0002212 ! negatively regulates is_a: RO:0012012 ! indirectly regulates transitive_over: RO:0002630 ! directly negatively regulates [Typedef] id: RO:0002410 name: causally related to def: "relation that links two events, processes, states, or objects such that one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly or wholly responsible for the effect, and the effect is partly or wholly dependent on the cause." [https://en.wikipedia.org/wiki/Causality] {type="owl:Axiom"} property_value: IAO:0000116 "This branch of the ontology deals with causal relations between entities. It is divided into two branches: causal relations between occurrents/processes, and causal relations between material entities. We take an 'activity flow-centric approach', with the former as primary, and define causal relations between material entities in terms of causal relations between occurrents.\n\nTo define causal relations in an activity-flow type network, we make use of 3 primitives:\n\n * Temporal: how do the intervals of the two occurrents relate? \n * Is the causal relation regulatory?\n * Is the influence positive or negative?\n\nThe first of these can be formalized in terms of the Allen Interval Algebra. Informally, the 3 bins we care about are 'direct', 'indirect' or overlapping. Note that all causal relations should be classified under a RO temporal relation (see the branch under 'temporally related to'). Note that all causal relations are temporal, but not all temporal relations are causal. Two occurrents can be related in time without being causally connected. We take causal influence to be primitive, elucidated as being such that has the upstream changed, some qualities of the donwstream would necessarily be modified.\n\nFor the second, we consider a relationship to be regulatory if the system in which the activities occur is capable of altering the relationship to achieve some objective. This could include changing the rate of production of a molecule.\n\nFor the third, we consider the effect of the upstream process on the output(s) of the downstream process. If the level of output is increased, or the rate of production of the output is increased, then the direction is increased. Direction can be positive, negative or neutral or capable of either direction. Two positives in succession yield a positive, two negatives in succession yield a positive, otherwise the default assumption is that the net effect is canceled and the influence is neutral.\n\nEach of these 3 primitives can be composed to yield a cross-product of different relation types." xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000232 "Do not use this relation directly. It is intended as a grouping for a diverse set of relations, all involving cause and effect." xsd:string [Typedef] id: RO:0002411 name: causally upstream of def: "p is causally upstream of q iff p is causally related to q, the end of p precedes the end of q, and p is not an occurrent part of q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_transitive: true is_a: BFO:0000063 ! precedes is_a: RO:0002418 ! causally upstream of or within [Typedef] id: RO:0002412 name: immediately causally upstream of def: "p is immediately causally upstream of q iff p is causally upstream of q, and the end of p is coincident with the beginning of q." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: RO:0002575 RO:0002411 is_a: RO:0002090 ! immediately precedes is_a: RO:0002411 ! causally upstream of [Typedef] id: RO:0002418 name: causally upstream of or within def: "p is 'causally upstream or within' q iff p is causally related to q, and the end of p precedes, or is coincident with, the end of q." [] synonym: "affects" RELATED [] property_value: IAO:0000116 "We would like to make this disjoint with 'preceded by', but this is prohibited in OWL2" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "influences (processual)" xsd:string is_transitive: true is_a: RO:0002501 ! causal relation between processes inverse_of: RO:0002427 ! causally downstream of or within [Typedef] id: RO:0002427 name: causally downstream of or within def: "inverse of causally upstream of or within" [] subset: RO:0002259 property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations is_transitive: true is_a: RO:0002501 ! causal relation between processes [Typedef] id: RO:0002428 name: involved in regulation of def: "c involved in regulation of p if c is involved in some p' and p' regulates some p" [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 holds_over_chain: RO:0002327 RO:0002211 holds_over_chain: RO:0002331 RO:0002211 is_a: RO:0002263 ! acts upstream of is_a: RO:0002431 ! involved in or involved in regulation of [Typedef] id: RO:0002429 name: involved in positive regulation of def: "c involved in regulation of p if c is involved in some p' and p' positively regulates some p" [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: RO:0004049 RO:0002428 holds_over_chain: RO:0002327 RO:0002213 holds_over_chain: RO:0002331 RO:0002213 is_a: RO:0002428 ! involved in regulation of [Typedef] id: RO:0002430 name: involved in negative regulation of def: "c involved in regulation of p if c is involved in some p' and p' negatively regulates some p" [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: RO:0004050 RO:0002428 holds_over_chain: RO:0002327 RO:0002212 holds_over_chain: RO:0002331 RO:0002212 is_a: RO:0002428 ! involved in regulation of [Typedef] id: RO:0002431 name: involved in or involved in regulation of def: "c involved in or regulates p if and only if either (i) c is involved in p or (ii) c is involved in regulation of p" [] property_value: IAO:0000116 "OWL does not allow defining object properties via a Union" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "involved in or reguates" xsd:string is_a: RO:0002264 ! acts upstream of or within is_a: RO:0002328 ! functionally related to is_a: RO:0002500 ! causal agent in process [Typedef] id: RO:0002434 name: interacts with def: "A relationship that holds between two entities in which the processes executed by the two entities are causally connected." [] subset: ro-eco synonym: "in pairwise interaction with" EXACT [] property_value: closeMatch "http://purl.obolibrary.org/obo/MI_0914" xsd:anyURI property_value: IAO:0000116 "Considering relabeling as 'pairwise interacts with'" xsd:anyURI property_value: IAO:0000116 "This relation and all sub-relations can be applied to either (1) pairs of entities that are interacting at any moment of time (2) populations or species of entity whose members have the disposition to interact (3) classes whose members have the disposition to interact." xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000232 "Note that this relationship type, and sub-relationship types may be redundant with process terms from other ontologies. For example, the symbiotic relationship hierarchy parallels GO. The relations are provided as a convenient shortcut. Consider using the more expressive processual form to capture your data. In the future, these relations will be linked to their cognate processes through rules." xsd:string property_value: seeAlso "http://purl.obolibrary.org/obo/ro/docs/interaction-relations/" xsd:anyURI domain: BFO:0000040 ! material entity range: BFO:0000040 ! material entity is_symmetric: true [Typedef] id: RO:0002436 name: molecularly interacts with def: "An interaction relationship in which the two partners are molecular entities that directly physically interact with each other for example via a stable binding interaction or a brief interaction during which one modifies the other." [] property_value: closeMatch "http://purl.obolibrary.org/obo/MI_0915" xsd:anyURI property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "binds" xsd:string property_value: IAO:0000118 "molecularly binds with" xsd:string property_value: seeAlso ECO:0000353 is_symmetric: true is_a: RO:0002434 ! interacts with [Typedef] id: RO:0002447 name: phosphorylates property_value: IAO:0000116 "Axiomatization to GO to be added later" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000118 "An interaction relation between x and y in which x catalyzes a reaction in which a phosphate group is added to y." xsd:string is_a: RO:0002436 ! molecularly interacts with [Typedef] id: RO:0002448 name: directly regulates activity of def: "The entity A, immediately upstream of the entity B, has an activity that regulates an activity performed by B. For example, A and B may be gene products and binding of B by A regulates the kinase activity of B.\n\nA and B can be physically interacting but not necessarily. Immediately upstream means there are no intermediate entity between A and B." [] synonym: "molecularly controls" EXACT [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000117 https://orcid.org/0000-0003-4639-4431 domain: BFO:0000040 ! material entity range: BFO:0000040 ! material entity is_a: RO:0002436 ! molecularly interacts with is_a: RO:0011002 ! regulates activity of [Typedef] id: RO:0002449 name: directly negatively regulates activity of def: "The entity A, immediately upstream of the entity B, has an activity that negatively regulates an activity performed by B. \nFor example, A and B may be gene products and binding of B by A negatively regulates the kinase activity of B." [] synonym: "molecularly decreases activity of" EXACT [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000117 https://orcid.org/0000-0003-4639-4431 property_value: IAO:0000118 "directly inhibits" xsd:string domain: BFO:0000040 ! material entity range: BFO:0000040 ! material entity is_a: RO:0002448 ! directly regulates activity of [Typedef] id: RO:0002450 name: directly positively regulates activity of def: "The entity A, immediately upstream of the entity B, has an activity that positively regulates an activity performed by B. \nFor example, A and B may be gene products and binding of B by A positively regulates the kinase activity of B." [] synonym: "molecularly increases activity of" EXACT [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000117 https://orcid.org/0000-0003-4639-4431 property_value: IAO:0000118 "directly activates" xsd:string domain: BFO:0000040 ! material entity range: BFO:0000040 ! material entity is_a: RO:0002448 ! directly regulates activity of [Typedef] id: RO:0002464 name: helper property (not for use in curation) property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000232 "This property or its subproperties is not to be used directly. These properties exist as helper properties that are used to support OWL reasoning." xsd:string [Typedef] id: RO:0002481 name: is kinase activity property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0002564 ! molecular interaction relation helper property [Typedef] id: RO:0002500 name: causal agent in process def: "A relationship between a material entity and a process where the material entity has some causal role that influences the process" [] property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations is_a: RO:0002595 ! causal relation between material entity and a process inverse_of: RO:0002608 ! process has causal agent [Typedef] id: RO:0002501 name: causal relation between processes def: "p is causally related to q if and only if p or any part of p and q or any part of q are linked by a chain of events where each event pair is one where the execution of p influences the execution of q. p may be upstream, downstream, part of, or a container of q." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000232 "Do not use this relation directly. It is intended as a grouping for a diverse set of relations, all involving cause and effect." xsd:string domain: BFO:0000003 ! occurrent range: BFO:0000003 ! occurrent is_a: RO:0002410 ! causally related to [Typedef] id: RO:0002502 name: depends on property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: seeAlso BFO:0000169 [Typedef] id: RO:0002506 name: causal relation between entities property_value: IAO:0000116 "The intent is that the process branch of the causal property hierarchy is primary (causal relations hold between occurrents/processes), and that the material branch is defined in terms of the process branch" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000232 "Do not use this relation directly. It is intended as a grouping for a diverse set of relations, all involving cause and effect." xsd:string domain: BFO:0000002 ! continuant range: BFO:0000002 ! continuant is_a: RO:0002410 ! causally related to [Typedef] id: RO:0002559 name: causally influenced by property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "causally influenced by (entity-centric)" xsd:string is_a: RO:0002506 ! causal relation between entities inverse_of: RO:0002566 ! causally influences [Typedef] id: RO:0002563 name: interaction relation helper property property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: seeAlso http://ontologydesignpatterns.org/wiki/Submissions:N-Ary_Relation_Pattern_%28OWL_2%29 property_value: seeAlso "http://purl.obolibrary.org/obo/ro/docs/interaction-relations/" xsd:anyURI is_a: RO:0002464 ! helper property (not for use in curation) [Typedef] id: RO:0002564 name: molecular interaction relation helper property property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 is_a: RO:0002563 ! interaction relation helper property [Typedef] id: RO:0002566 name: causally influences def: "The entity or characteristic A is causally upstream of the entity or characteristic B, A having an effect on B. An entity corresponds to any biological type of entity as long as a mass is measurable. A characteristic corresponds to a particular specificity of an entity (e.g., phenotype, shape, size)." [] property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000117 https://orcid.org/0000-0003-4639-4431 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "causally influences (entity-centric)" xsd:string domain: BFO:0000002 ! continuant range: BFO:0000002 ! continuant is_a: RO:0002506 ! causal relation between entities [Typedef] id: RO:0002578 name: directly regulates def: "p directly regulates q iff p is immediately causally upstream of q and p regulates q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "directly regulates (processual)" xsd:string property_value: RO:0002575 RO:0002211 is_a: RO:0002211 ! regulates is_a: RO:0002412 ! immediately causally upstream of [Typedef] id: RO:0002584 name: has part structure that is capable of def: "s 'has part structure that is capable of' p if and only if there exists some part x such that s 'has part' x and x 'capable of' p" [] property_value: IAO:0000112 "gland SubClassOf 'has part structure that is capable of' some 'secretion by cell'" xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 holds_over_chain: BFO:0000051 RO:0002215 is_a: RO:0002328 ! functionally related to is_a: RO:0002595 ! causal relation between material entity and a process [Typedef] id: RO:0002595 name: causal relation between material entity and a process def: "A relationship that holds between a material entity and a process in which causality is involved, with either the material entity or some part of the material entity exerting some influence over the process, or the process influencing some aspect of the material entity." [] property_value: IAO:0000116 "Do not use this relation directly. It is intended as a grouping for a diverse set of relations, all involving cause and effect." xsd:string property_value: IAO:0000117 https://orcid.org/0000-0002-6601-2165 property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations domain: BFO:0000040 ! material entity range: BFO:0000015 ! process is_a: RO:0002410 ! causally related to [Typedef] id: RO:0002596 name: capable of regulating def: "Holds between c and p if and only if c is capable of some activity a, and a regulates p." [] property_value: IAO:0000112 "pyrethroid -> growth" xsd:string property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations holds_over_chain: RO:0002215 RO:0002211 is_a: RO:0002500 ! causal agent in process [Typedef] id: RO:0002597 name: capable of negatively regulating def: "Holds between c and p if and only if c is capable of some activity a, and a negatively regulates p." [] property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations holds_over_chain: RO:0002215 RO:0002212 is_a: RO:0002596 ! capable of regulating [Typedef] id: RO:0002598 name: capable of positively regulating def: "Holds between c and p if and only if c is capable of some activity a, and a positively regulates p." [] property_value: IAO:0000112 "renin -> arteriolar smooth muscle contraction" xsd:string property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations holds_over_chain: RO:0002215 RO:0002213 is_a: RO:0002596 ! capable of regulating [Typedef] id: RO:0002608 name: process has causal agent def: "Inverse of 'causal agent in process'" [] property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations is_a: RO:0002410 ! causally related to [Typedef] id: RO:0002629 name: directly positively regulates def: "p directly positively regulates q iff p is immediately causally upstream of q, and p positively regulates q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "directly positively regulates (process to process)" xsd:string property_value: RO:0004049 RO:0002578 is_a: RO:0002213 ! positively regulates is_a: RO:0002578 ! directly regulates [Typedef] id: RO:0002630 name: directly negatively regulates def: "p directly negatively regulates q iff p is immediately causally upstream of q, and p negatively regulates q." [] subset: http://purl.obolibrary.org/obo/valid_for_go_annotation_extension subset: http://purl.obolibrary.org/obo/valid_for_go_ontology subset: http://purl.obolibrary.org/obo/valid_for_gocam property_value: IAO:0000119 http://purl.obolibrary.org/obo/ro/docs/causal-relations property_value: IAO:0000589 "directly negatively regulates (process to process)" xsd:string property_value: RO:0004050 RO:0002578 is_a: RO:0002212 ! negatively regulates is_a: RO:0002578 ! directly regulates [Typedef] id: RO:0004031 name: enables subfunction def: "Holds between an entity and an process P where the entity enables some larger compound process, and that larger process has-part P." [] holds_over_chain: RO:0002327 BFO:0000051 is_a: RO:0002328 ! functionally related to created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-01-25T23:20:13Z [Typedef] id: RO:0004032 name: acts upstream of or within, positive effect subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: RO:0004049 RO:0002264 property_value: seeAlso http://wiki.geneontology.org/index.php/Acts_upstream_of_or_within,_positive_effect holds_over_chain: RO:0002327 RO:0004047 is_a: RO:0002264 ! acts upstream of or within created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-01-26T23:49:30Z [Typedef] id: RO:0004033 name: acts upstream of or within, negative effect subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: RO:0004050 RO:0002264 holds_over_chain: RO:0002327 RO:0004046 is_a: RO:0002264 ! acts upstream of or within created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-01-26T23:49:51Z [Typedef] id: RO:0004034 name: acts upstream of, positive effect def: "c 'acts upstream of, positive effect' p if c is enables f, and f is causally upstream of p, and the direction of f is positive" [] subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: RO:0004049 RO:0002263 property_value: seeAlso http://wiki.geneontology.org/index.php/Acts_upstream_of,_positive_effect holds_over_chain: RO:0002327 RO:0002304 is_a: RO:0002263 ! acts upstream of is_a: RO:0004032 ! acts upstream of or within, positive effect created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-01-26T23:53:14Z [Typedef] id: RO:0004035 name: acts upstream of, negative effect def: "c 'acts upstream of, negative effect' p if c is enables f, and f is causally upstream of p, and the direction of f is negative" [] subset: http://purl.obolibrary.org/obo/valid_for_go_gp2term property_value: RO:0004050 RO:0002263 property_value: seeAlso http://wiki.geneontology.org/index.php/Acts_upstream_of,_negative_effect holds_over_chain: RO:0002327 RO:0002305 is_a: RO:0002263 ! acts upstream of is_a: RO:0004033 ! acts upstream of or within, negative effect created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-01-26T23:53:22Z [Typedef] id: RO:0004046 name: causally upstream of or within, negative effect property_value: RO:0004050 RO:0002418 is_a: RO:0002418 ! causally upstream of or within created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-03-13T23:55:05Z [Typedef] id: RO:0004047 name: causally upstream of or within, positive effect property_value: RO:0004049 RO:0002418 is_a: RO:0002418 ! causally upstream of or within created_by: https://orcid.org/0000-0002-6601-2165 creation_date: 2018-03-13T23:55:19Z [Typedef] id: RO:0011002 name: regulates activity of def: "The entity A has an activity that regulates an activity of the entity B. For example, A and B are gene products where the catalytic activity of A regulates the kinase activity of B." [] property_value: IAO:0000117 https://orcid.org/0000-0003-4639-4431 domain: BFO:0000040 ! material entity range: BFO:0000040 ! material entity is_a: RO:0002566 ! causally influences [Typedef] id: RO:0012011 name: indirectly causally upstream of def: "p is indirectly causally upstream of q iff p is causally upstream of q and there exists some process r such that p is causally upstream of r and r is causally upstream of q." [] is_a: RO:0002411 ! causally upstream of created_by: pg creation_date: 2022-09-26T06:07:17Z [Typedef] id: RO:0012012 name: indirectly regulates def: "p indirectly regulates q iff p is indirectly causally upstream of q and p regulates q." [] is_a: RO:0002211 ! regulates is_a: RO:0012011 ! indirectly causally upstream of created_by: pg creation_date: 2022-09-26T06:08:01Z [Typedef] id: RO:0017001 name: device utilizes material def: "X device utilizes material Y means X and Y are material entities, and X is capable of some process P that has input Y." [] synonym: "utilizes" BROAD [] property_value: IAO:0000112 "A diagnostic testing device utilizes a specimen." xsd:string property_value: IAO:0000117 https://orcid.org/0000-0001-9625-1899 property_value: IAO:0000117 https://orcid.org/0000-0003-2620-0345 property_value: IAO:0000232 "A diagnostic testing device utilizes a specimen means that the diagnostic testing device is capable of an assay, and this assay a specimen as its input." xsd:string property_value: IAO:0000232 "See github ticket https://github.com/oborel/obo-relations/issues/497" xsd:string holds_over_chain: RO:0002215 RO:0002233 creation_date: 2021-11-08T12:00:00Z [Typedef] id: RO:0019000 name: regulates characteristic def: "A relationship that holds between a process and a characteristic in which process (P) regulates characteristic (C) iff: P results in the existence of C OR affects the intensity or magnitude of C." [] property_value: http://purl.org/dc/terms/contributor https://orcid.org/0000-0002-8688-6599 domain: BFO:0000015 ! process range: PATO:0000001 ! quality holds_over_chain: RO:0002211 RO:0019000 is_a: RO:0002410 ! causally related to [Typedef] id: RO:0019001 name: positively regulates characteristic def: "A relationship that holds between a process and a characteristic in which process (P) positively regulates characteristic (C) iff: P results in an increase in the intensity or magnitude of C." [] property_value: http://purl.org/dc/terms/contributor https://orcid.org/0000-0002-8688-6599 holds_over_chain: RO:0002213 RO:0019001 is_a: RO:0019000 ! regulates characteristic [Typedef] id: RO:0019002 name: negatively regulates characteristic def: "A relationship that holds between a process and a characteristic in which process (P) negatively regulates characteristic (C) iff: P results in a decrease in the intensity or magnitude of C." [] property_value: http://purl.org/dc/terms/contributor https://orcid.org/0000-0002-8688-6599 holds_over_chain: RO:0002212 RO:0019001 holds_over_chain: RO:0002213 RO:0019002 is_a: RO:0019000 ! regulates characteristic [Typedef] id: SWO:0000131 name: directly preceded by def: "Entity A is 'directly preceded by' entity B if there are no intermediate entities temporally between the two entities. WIthin SWO this property is mainly used to describe versions of entities such as software." [] property_value: http://purl.org/dc/terms/creator "OBO Foundry" xsd:string property_value: IAO:0000112 "Microsoft version 2007 is directly preceded by Microsoft version 2003." xsd:string property_value: IAO:0000119 "Allyson Lister" xsd:string is_a: SWO:0000301 ! follows inverse_of: SWO:0000132 ! directly followed by [Typedef] id: SWO:0000132 name: directly followed by def: "'directly followed by' is an object property which further specializes the parent 'followed by' property. In the assertion 'C directly followed by C1', says that Cs generally are immediately followed by C1s." [] property_value: IAO:0000119 "Allyson Lister" xsd:string is_a: SWO:0000300 ! followed by [Typedef] id: SWO:0000300 name: followed by comment: AL 2.9.22: When incorporating all BFO annotations, it became clear that we were using BFO 'precedes' (which was the original parent for 'directly followed by') incorrectly. 'precedes' has a range and domain of occurent, and the IAO version number class is an ICE, which is a continuant. This led to an inconsistent ontology. To fix this for now, we have created a new class that performs a similar function to BFO 'precedes' but without the domain/range restrictions. [Typedef] id: SWO:0000301 name: follows comment: AL 2.9.22: When incorporating all BFO annotations, it became clear that we were using BFO 'precedes' and 'preceded by' (which was the original parent for 'directly preceded by') incorrectly. 'precedes' has a range and domain of occurent, and the IAO version number class is an ICE, which is a continuant. This led to an inconsistent ontology. To fix this for now, we have created a new class (and this, its inverse) that performs a similar function to BFO 'precedes' but without the domain/range restrictions. [Typedef] id: SWO:0000741 name: is encoded in def: "Is encoded in is an \"is about\" relationship which describes the type of encoding used for the referenced class." [] property_value: IAO:0000112 "Linking a type of software to its particular programming language." xsd:string property_value: IAO:0000119 "Allyson Lister" xsd:string is_a: IAO:0000136 ! is about