IRI skos:prefLabel skos:definition dc:source skos:example rdfs:comment altLabel Parent ID A skos:prefLabel A skos:definition AI dc:source A skos:example A rdfs:comment A skos:altLabel SPLIT=| SC % semapv:MappingActivity mapping activity A process that relates to the creation, confirmation, rejection or curation of a mapping. Matching is a mapping activity that results in the creating of a mapping; mapping review is an activity that results in the confirmation of a mapping. semapv:Matching matching process An process that results in a mapping between a subject and an object entity. The label of a subject entity matches to an exact synonym of an object entity. matching operation|matching task semapv:MappingActivity semapv:Mapping mapping A triple comprising a subject entity s, an object entity o and a mapping predicate p. The subject entity NCI:C9305 is mapped to the object entity ICD10:C80.9 using the skos:relatedMatch mapping predicate. semapv:LexicalMatching lexical matching process A matching process based on a lexical comparison between one or more syntactic features of the subject with one or more syntactic features of the object. The label of a subject entity matches to an exact synonym of an object entity. semapv:Matching semapv:LogicalReasoning logical reasoning matching process A matching process based on the inferences made by a logical reasoner. Two classes across ontologies are determined equivalent by an OWL reasoner such as ELK. semapv:Matching semapv:CompositeMatching composite matching process A matching process based on multiple, possibly intertwined, matching approaches. An ontology matching tool determines that a subject should be mapped to an object by employing a range of techniques, including lexical, semantic and structural. semapv:Matching semapv:UnspecifiedMatching unspecified matching process A matching process based on an unspecified comparison. A mapping between a subject and an object was established, but it is no longer clear how or why. semapv:Matching semapv:SemanticSimilarityThresholdMatching semantic similarity threshold-based matching process A matching process based on a minimum threshold of a score from a comparison based on a semantic similarity algorithm. A match between a subject and an object entity was established because they had a Jaccard score higher than 0.8 based on the set of (common) superclasses. semapv:Matching semapv:LexicalSimilarityThresholdMatching lexical similarity threshold-based matching process A lexical matching process based on a minimum threshold of a score from a comparison based on a lexical similarity algorithm. A match between a subject and an object was established because they had a Levenshtein score higher than 0.8. semapv:Matching semapv:StructuralMatching structural matching process https://doi.org/10.1016/j.websem.2009.11.002 A match between a subject and an object was established because of the similarity of their structural features, e.g., the number of direct property of a class. "Structural matching does not involve looking at ""values"" of properties." semapv:Matching semapv:InstanceBasedMatching instance-based matching process A matching process based on individual representations (or instances). https://doi.org/10.1007/978-3-642-38721-0 A match between a subject A and an object B was established because they share the same instances. semapv:Matching semapv:BackgroundKnowledgeBasedMatching background knowledge-based matching process A matching process that exploits background knowledge from external resources, commonly referred to as background knowledge resources. This approach is also known as indirect matching, BK-based matching or context-based matching. https://doi.org/10.1016/j.websem.2018.04.001 A match between a subject A and an object B was established because they appear equivalent under consideration of externally provided background knowledge. semapv:Matching semapv:MappingChaining mapping chaining-based matching process A matching process based on the traversing of multiple mappings. A match between a subject A and an object B was established because A was mapped to C, C was mapped to D and D was mapped to B. semapv:Matching semapv:MappingInversion mapping inversion-based matching process A matching process based on the inverting or flipping of the subject with the object of a mapping in accordance with the semantics of the mapping predicate. https://mapping-commons.github.io/sssom/chaining_rules/ A broad match between a subject A and an object B using the skos:narrowMatch mapping relation was established because B was mapped to A using the skos:broadMatch mapping relation. mapping flipping|semantic walk-backs|mapping inversion|mapping inverting semapv:Matching semapv:MappingReview mapping review A process that is concerned with determining if a mapping “candidate” (otherwise determined) is reasonable/correct. A match between a subject A and an object B was established elsewhere, and a human reviewer determined that the mapping is true (or false) based on an independent evaluation. semapv:MappingActivity semapv:ManualMappingCuration manual mapping curation A matching process that is performed by a human agent and is based on human judgement and domain knowledge. A human curator determines that a subject should be mapped to an object by virtue of their domain expertise. semapv:Matching semapv:MatchingPreprocessing matching process pre-processing A preliminary processing of inputs prior to performing matching. semapv:MappingActivity semapv:Normalization normalization A family of preprocessing techniques for reducing strings to be compared to a common format. https://doi.org/10.1007/978-3-642-38721-0 semapv:MatchingPreprocessing semapv:Stemming stemming The process of reducing a word to its word stem. semapv:Normalization semapv:Lemmatization lemmatization The process of reducing the different forms of a word to one single form. https://doi.org/10.1007/978-3-642-38721-0 semapv:Normalization semapv:StopWordRemoval stop-word removal A preprocessing method transforming a string by removing a list of stop words. https://doi.org/10.1007/978-3-642-38721-0 The stop-words “of, and, is, a” are removed from the label field of the subject entity in the mapping. semapv:Normalization semapv:RegexRemoval regex removal A preprocessing method transforming a string by matching a regular expression and then removing that match. The regex match “[ -]phenotype” is removed from the label field of the subject entity in the mapping. semapv:Normalization semapv:RegexReplacement regex replacement A preprocessing method transforming a string by matching a regular expression and then replacing that match with a specified substitution string. The regex match “[ -]phenotype” is replaced by “-disease” for the label field of the subject entity in the mapping. semapv:Normalization semapv:MatchingPostProcessing matching process post-processing A process applied to a set of mappings, usually with the intention of changing it. semapv:MappingActivity semapv:ThresholdFiltering threshold filtering semapv:MatchingPostProcessing semapv:StableMarriageFiltering stable marriage filtering semapv:MatchingPostProcessing semapv:CardinalityFiltering cardinality filtering semapv:MatchingPostProcessing semapv:LogicalConsistencyFiltering logical consistency filtering semapv:MatchingPostProcessing semapv:CaseNormalization case normalization A normalization technique converting each alphabetic character in a string to their lower case counterpart. https://doi.org/10.1007/978-3-642-38721-0 The string 'Ontology' is normalised to 'ontology' (lower case). semapv:Normalization semapv:DiacriticsSuppression diacritics suppression A normalization technique replacing diactritic signs (e.g. accents, hats) with their most frequent replacements. https://doi.org/10.1007/978-3-642-38721-0 The string 'Protégé' is normalised to 'Protege'. semapv:Normalization semapv:BlankNormalisation blank normalisation A normalization technique replacing all blank characters, such as space, tabulation, carriage return (or sequences of these) into a single blank character. https://doi.org/10.1007/978-3-642-38721-0 The string 'ontology matching' (two spaces between 'ontology' and 'matching') becomes 'ontology matching' (one space). semapv:Normalization semapv:LinkStripping link stripping A normalization technique replacing specific links between words, such as apostrophes, dashes, underscore, etc into dashes or blanks. https://doi.org/10.1007/978-3-642-38721-0 The string 'alzheimer's' becomes 'alzheimers'. semapv:Normalization semapv:DigitSuppression digit suppression A normalization technique removing all numbers in a string. https://doi.org/10.1007/978-3-642-38721-0 The string 'ontology1' becomes 'ontology'. semapv:Normalization semapv:PunctuationElemination punctuation elimination A normalization technique removing all punctation characters from a string. https://doi.org/10.1007/978-3-642-38721-0 The string 'e.g.' becomes 'eg'. semapv:Normalization semapv:SimilarityMeasure similarity measure A technique for determining a score that characterises the similarity between two entities. https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure string-based similarity measure A similarity measure based on the comparison of strings. https://doi.org/10.1007/978-3-642-38721-0 semapv:SimilarityMeasure semapv:StringEquality string equality A string-based similarity measure which involves determining if two strings associated with mapping entities are equal. https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:HammingDistance hamming distance https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:SubstringTest substring test https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:SubstringSimilarity substring similarity https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:nGramSimilarity ngram similarity https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:EditDistance edit distance https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:LevenshteinEditDistance levenshtein distance https://doi.org/10.1007/978-3-642-38721-0 semapv:EditDistance semapv:TokenBasedDistance token-based distance https://doi.org/10.1007/978-3-642-38721-0 semapv:StringBasedSimilarityMeasure semapv:LanguageBasedSimilarityMeasure language-based similarity measure https://doi.org/10.1007/978-3-642-38721-0 semapv:SimilarityMeasure semapv:Tokenization tokenization https://doi.org/10.1007/978-3-642-38721-0 semapv:Normalization semapv:TermExtraction term extraction https://doi.org/10.1007/978-3-642-38721-0 named entity recognition semapv:Normalization semapv:BoundedPathMatching bounded path matching A structural matching process based on the comparison of the matched super- and subclass paths (up to a bounded depth) of two candidate entities. https://doi.org/10.1007/978-3-642-38721-0 Two classes are matched because their direct superclasses and subclasses (within a bounded depth) are already paired by other matchers, and the structural agreement among neighbours raises confidence in the match. "Described as a structural matching technique in Euzenat & Shvaiko, ""Ontology Matching"" (2nd ed., 2013), and implemented in classical structural OM systems such as Anchor-PROMPT." semapv:StructuralMatching semapv:MachineLearningBasedMatching machine learning-based matching process A matching process in which matches between entities are predicted by machine learning models trained on labelled data or learned representations. https://doi.org/10.1145/511446.511532 A trained machine learning model classifies a pair of candidate entities as matching or not based on learned features; the OntoAligner toolkit packages a number of such models. ML-based matching semapv:Matching semapv:EmbeddingBasedMatching embedding-based matching process A machine learning-based matching process in which matches are identified by comparing learned vector representations (embeddings) of entities under a vector similarity measure. https://doi.org/10.48550/arXiv.2310.11244 A sentence-transformer encodes the label of each candidate entity; the pair is reported as a match when the cosine similarity between the two embeddings exceeds a fixed threshold. Where embedding similarity is used only to generate candidate pairs for a downstream matcher (rather than to make the final match decision), the process is more accurately classified as semapv:EmbeddingBasedBlocking. embedding similarity-based matching semapv:MachineLearningBasedMatching semapv:TransformerBasedMatching transformer-based matching process An embedding-based matching process that uses transformer models to produce contextual vector representations of entity features for similarity comparison. https://doi.org/10.48550/arXiv.2112.02682 BERTMap fine-tunes BERT on synonym pairs derived from the input ontologies and matches classes whose fine-tuned contextual embeddings have high cosine similarity. Transformer models are a deep learning architecture designed to handle sequential data, particularly for natural language processing tasks. semapv:EmbeddingBasedMatching semapv:GraphRepresentationLearningBasedMatching graph representation learning-based matching process An embedding-based matching process in which matches are identified by comparing learned vector representations that encode the structural and semantic features of entities and their surrounding graph context. https://doi.org/10.48550/arXiv.2503.21902 A graph embedding method such as node2vec, GraphSAGE, or OWL2Vec* encodes each entity together with its neighbourhood in the ontology graph; matches are produced where the resulting embeddings are close under a similarity measure. graph embedding-based matching|graph machine learning-based matching semapv:EmbeddingBasedMatching semapv:LLMBasedMatching LLM-based matching process A machine learning-based matching process in which a large language model determines matches between entities, typically through single-step or few-shot prompting, or through fine-tuning the model to judge mapping candidates or generate mappings directly. https://doi.org/10.1145/3587259.3627571 OLaLa prompts a large language model with the labels and surrounding context of two candidate entities and uses the model's yes/no decision as the match outcome. Where an LLM is used only to produce embeddings that are then compared by similarity, the process is an embedding-based (transformer-based) matching process rather than an LLM-based one. The classifier is how the model is used, not which model. Multi-step, tool-using, autonomous workflows driven by an LLM are captured by semapv:AgentBasedMatching. large language model-based matching semapv:MachineLearningBasedMatching semapv:AgentBasedMatching agent-based matching process A matching process in which an autonomous agent plans and executes a multi-step workflow — typically involving tool use, retrieval, intermediate reasoning, and self-evaluation — to determine matches between entities. The agent decides at runtime what information to gather, which techniques to apply, and when to stop. https://doi.org/10.48550/arXiv.2503.21902 An agent is given a pair of candidate entities and autonomously decides to look up their labels, query an external knowledge base, retrieve definitions, compare them with a similarity measure, and revise its judgement before producing a final match decision; the OntoAligner toolkit provides agentic matching of this kind. Distinguished from single-step LLM-based matching by the agent's autonomy over its own process. Distinguished from composite matching, which combines heterogeneous matching techniques under a fixed pipeline, by the agent's runtime control over what to do next. In current practice, agent-based matchers are predominantly driven by large language models, but this class does not commit to a particular agent technology — a specific process may also be classified as semapv:LLMBasedMatching (or any other matching class) where appropriate. agentic matching|agentic ontology matching semapv:Matching semapv:Blocking blocking A mapping activity that, given two sets of input entities, produces a reduced set of candidate entity pairs likely to match — favouring recall over precision — to be passed on to a downstream matching process. https://doi.org/10.1007/978-3-642-38721-0 Out of 10^7 possible subject–object pairs, a blocker returns 10^5 candidate pairs by applying inexpensive comparisons such as token overlap, hash buckets, or embedding-space nearest-neighbour search. Blocking is conceptually distinct from matching: a blocker is recall-oriented and typically encodes each entity independently of the others, whereas a matcher is precision-oriented and decides about a specific candidate pair. Many modern pipelines combine an embedding-based blocker with a transformer- or LLM-based matcher. candidate generation|candidate retrieval semapv:MappingActivity semapv:EmbeddingBasedBlocking embedding-based blocking A blocking process in which entities are encoded independently as vector embeddings and candidate pairs are produced by nearest-neighbour search or similarity-threshold filtering in the embedding space. https://doi.org/10.48550/arXiv.2310.11244 A sentence-transformer encodes every subject and object label into a vector; an approximate nearest-neighbour index (e.g. FAISS) returns, for each object, the top-k subjects whose embeddings are closest in cosine distance, as the set of candidate pairs to be matched downstream. bi-encoder blocking|embedding-based candidate generation semapv:Blocking