ISWC2012 Session Chair
2
Palmonari Matteo
48a74a474d907edb77a0cdb272adea5767df0be2
University of Milano-Bicocca
Matteo
Palmonari
Palmonari Matteo
Mari Carmen
Mari Carmen Suárez-Figueroa
f9494ff417ea83c9d0fb4ddb0f4bb8b2c76b0b22
Suárez-Figueroa
Mari Carmen Suárez-Figueroa
Universidad Politecnica de Madrid
ISWC2012 Session Chair
Mounir Mokhtari
d9dca0483d8e3ef877c81510e0e396805f910c41
Image & Pervasive Access Lab (IPAL), UMI CNRS
Mokhtari
Mounir
Mounir Mokhtari
Monash University
Monash University
Sambhawa Priya
974b04e6905f6a70f94024e5973cb6722ea9ef60
Lehigh University
Priya
Sambhawa
Sambhawa Priya
VU Amsterdam
Willem Robert
8985bbc3abd3830d25e6ed541b7bb1942eb85c27
Van Hage
Willem Robert Van Hage
Willem Robert Van Hage
In this paper, we present a doctoral thesis which introduces a new approach of time series enrichment with semantics. The paper shows the problem of assigning time series data to the right party of interest and why this problem could not be solved so far. We demonstrate a new way of processing semantic time series and the consequential ability of addressing users. The combination of time series processing and Semantic Web technologies leads us to a new powerful method of data processing and data generation, which offers completely new opportunities to the expert user.
A Multi-Domain Framework for Community Building Based on Data Tagging
A Multi-Domain Framework for Community Building Based on Data Tagging
Bojan Bozic
76500430
A Multi-Domain Framework for Community Building Based on Data Tagging
76500430
Bernardo Magnini
e0676e9f8b3753600d1a31d11da3abc1d40c3281
Fondazione Bruno Kessler
Magnini
Bernardo
Bernardo Magnini
David Karger
MIT
Karger
David
David Karger
Naoki Fukuta
460aee02e16bc8ef5d71fc487dc4654ee22e8b9e
Shizuoka University
Fukuta
Naoki
Naoki Fukuta
Conor Hayes
e3e11c1ce25fbaccc08ed9085e4ccd882a79a8f1
DERI, NUI Galway
Hayes
Conor
Conor Hayes
GESIS - Leibniz Institute for the Social Sciences
GESIS - Leibniz Institute for the Social Sciences
Naimdjon Takhirov
2108ae71bf373f4d15c01a5a18fff20ae0b5c60a
Norwegian University of Science and Technology
Takhirov
Naimdjon
Naimdjon Takhirov
76500294
Linked Stream Data Processing Engines: Facts and Figures
Linked Stream Data
Linked Stream Data Processing Engines: Facts and Figures
Linked Stream Data
Benchmarking
Danh Le Phuoc, Minh Dao-Tran, Minh-Duc Pham, Peter Boncz, Thomas Eiter and Michael Fink
Benchmarking
Linked Stream Data Processing
Linked Stream Data Processing Engines: Facts and Figures
76500294
Linked Stream Data,Linked Stream Data Processing,Benchmarking
Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines.
Linked Stream Data Processing
David Huynh
David
Huynh
686042aaadc71e7514c0e7fefaf5c15d32abc9d2
David Huynh
Google
Matthias Thimm
a46d3da4820a0df9ba0426479e35331985557bfd
University of Koblenz and Landau
Thimm
Matthias
Matthias Thimm
Thomas Hubauer
8fb159e64b68a46283679910a3afba8a0ce8da53
Siemens AG, Corporate Technology
Hubauer
Thomas
Thomas Hubauer
ISWC2012 Session Chair
Clark & Parsia
Clark & Parsia
1
Nick
8c1f1d8aa5613b419a5f3716110ce9dcd3a8946f
Bassiliades
Nick Bassiliades
Nick Bassiliades
Aristotle University of Thessaloniki
Paolo Ciancarini
f2918e17b4b356eb9731b54b0fd710dbfede6681
University of Bologna
Ciancarini
Paolo
Paolo Ciancarini
ISWC2012 Session Chair
Thomas Schandl
a800b79dc962704d9731653a26e0b45f744674ba
Semantic Web Company GmbH
Schandl
Thomas
Thomas Schandl
Workshop Chair
Tudor
University of Queensland
Tudor Groza
Groza
2ddece58ea61e1d34878e723a8af024242daec80
Tudor Groza
3
Jans Aasman
203216e2412e287bad61ff368f0b51f41cc73311
Franz
Aasman
Jans
Jans Aasman
Ryutaro Ichise
0a09da4b327971fff4a6573caac2b4872cab5cc9
National Institute of Informatics
Ichise
Ryutaro
Ryutaro Ichise
ISWC2012 Session Chair
Makoto Nakatsuji
676bbe0a08c39b31531c2543d7d3a6cd11ee1090
NTT
Nakatsuji
Makoto
Makoto Nakatsuji
University of New South Wales
University of New South Wales
National Observatory of Athens
National Observatory of Athens
Harith
ad0c7d68490b84d6c7f8b0cb8aa1e457559386ef
Harith Alani
Harith Alani
Alani
KMi, The Open University
CERN
CERN
Krishnaprasad Thirunarayan
4244f1f45d6d9bb6d4a3e608db0d856e1d005715
Thirunarayan
Krishnaprasad Thirunarayan
Krishnaprasad
e70f6742afd3d6c1b6eebb41e0ae7cfa1f150108
Kno.e.sis Center, Wright State University
OCLC
OCLC
University of Texas at Dallas
University of Texas at Dallas
University of Maryland
Louiqa Raschid
Louiqa
Raschid
Louiqa Raschid
26e37b130fe1f4cfce90f9db362074b90b9e6de2
Siemens AG Österreich
Siemens AG Österreich
16795
2012-11-11T16:37:43-0800
iswc2012 metadata v20121111
2012-11-12T09:00:00+05:00
Scalability
Linked Data
Linked Science
Scalability
Big Data
Open Science
The 2nd International Workshop on Linked Science 2012Tackling Big Data
Open Science
Linked Data
Linked Science
Scientific communication has traditionally relied upon publications and presentations, with an estimate of millions of publications worldwide per year; the growth rate of PubMed alone is now 1 paper per minute. The results described in these articles are often backed by large amounts of diverse data produced by complex experiments, computer simulations, and observations of physical phenomena. Because of this avalanche of data, it is increasingly hard to validate, reproduce, reuse and leverage scientific data. In addition, although publications, methods and datasets are very related, they are not easily accessible and interlinked. The notable exception is omics research where journals require deposit of sequences in databanks as a condition of publication. Even where data is discoverable and accessible, significant challenges remain in data reuse and sharing, in facilitating the necessary correlation, integration and synthesis of data across levels of theory, techniques and disciplines. In the 2nd International Workshop on Linked Science (LISC2012) we will discuss and present results of new ways of publishing, sharing and linking scientific data together, and reasoning over such data to discover interesting new links to validate research. The theme of this years workshop will focus on research addressing these issues with respect to big data. Big Data is loosely characterized by the size and/or number of individual files, the number of represented variables, a range of physical scales, a range of scientific disciplines, heterogeneous metadata and data formats, in short data that cannot easily be accessed and manipulated from a thumb-drive.
2012-11-12T17:30:00+05:00
Linked Science, Big Data, Linked Data, Open Science, Scientific Communication, Scalability
LISC-2012
Scientific Communication
Scientific Communication
Big Data
Kristian Slabbekoorn
da200b97b2b21aaf4a5412730368cd88137dc3b7
Tokyo Institute of Technology
Slabbekoorn
Kristian
Kristian Slabbekoorn
Veronica Rizzi
University of Trento
Rizzi
Veronica
Veronica Rizzi
University of Illinois at Chicago
University of Illinois at Chicago
Daniel Bär
066e6b9ecbe25dddc46b3fc71e51f161cfb6ed6f
TU Darmstadt
Bär
Daniel
Daniel Bär
Mohsen Taheriyan
c4790d48328a9f0b42b299a6b6135c0cb9ce7c4e
Information Sciences Institute, University of Southern California
Taheriyan
Mohsen
Mohsen Taheriyan
National and Kapodistrian University of Athens
a45138009890ce780bd31806e8888b8a2f26d1af
Kostis
Kyzirakos
Kostis Kyzirakos
Kostis Kyzirakos
forward look
forward look
2
1
CMU
CMU
35a8d4858ba240996a6f71836d93fbfdcd2b4843
Axel
Siemens AG Österreich; DERI, NUI Galway
Axel Polleres
Polleres
Axel Polleres
Universidade Nova de Lisboa
Universidade Nova de Lisboa
RDFS inference,Parallel reasoning,Scalable reasoning
Parallel reasoning
RDFS inference
Reasoning in RDFS is Inherently Serial, at least in the worst case
Although it appears that reasoning in RDFS is embarrassingly parallel, this is not the case. Because all vocabulary is treated the same way in RDF, it is possible to extend the RDFS ontology vocabulary. The ability permits the creation of useful constructs that are not amenable to parallelism, and that in the end require serial processing.
RDFS inference
Reasoning in RDFS is Inherently Serial, at least in the worst case
Peter Patel-Schneider
Parallel reasoning
Scalable reasoning
Reasoning in RDFS is Inherently Serial, at least in the worst case
Scalable reasoning
University of Koblenz and Landau
University of Koblenz and Landau
ISWC2012 Session Chair
Microsoft Research
Microsoft Research
Dario Cerizza
f12618752715ccd047ba2db8e6f97087a50502a9
Politecnico di Milano
Cerizza
Dario
Dario Cerizza
Nokia Services
Nokia Services
Haofen
Shanghai Jiao Tong University
Haofen Wang
0233b9fd11287c2f4beecdd5a3002eb5cae9f080
Wang
Haofen Wang
Hepp
49e06491d1c02eead2d362e2300fa56d24ed5213
Martin Hepp
Martin
Bundeswehr University Munich
Martin Hepp
3
Edna Ruckhaus
8d09d177c2154e6e9e32d56c86a6d64d302afcac
Universidad Simon Bolivar
Edna
Edna Ruckhaus
Ruckhaus
Knowledge Pattern Extraction and their usage in Exploratory Search
Andrea Giovanni Nuzzolese
76500438
Knowledge Pattern Extraction and their usage in Exploratory Search
Knowledge interaction in Web context is a challenging problem. For instance, it requires to deal with complex structures able to filter knowledge by drawing a meaningful context boundary around data. We assume that these complex structures can be formalized as Knowledge Patterns (KPs), aka frames. This Ph.D. work is aimed at developing methods for extracting KPs from the Web and at applying KPs to exploratory search tasks. We want to extract KPs by analyzing the structure of Web links from rich resources, such as Wikipedia.
76500438
Knowledge Pattern Extraction and their usage in Exploratory Search
Amar Djalil Mezaour
61370d2392cdeb675cf36c3dacbe74dbd28d686f
EXALEAD, Dassault Systèmes
Mezaour
Amar Djalil
Amar Djalil Mezaour
John
Google
John Gianandrea
John Gianandrea
John Giannandrea, Director of Engineering at Google, leads the http://www.freebase.com/ project, an open database of knowledge which anyone can contribute to. Freebase was created by Metaweb Technologies, which John founded and which was acquired by Google in 2010. Prior to Metaweb, John co-founded Tellme Networks and was the chief technologist of Netscapes browser group where he contributed to many industry standards including HTML, HTTP, SSL, Java and RDF. John is originally from Scotland and graduated from Strathclyde University, Glasgow.
Gianandrea
Antidot
Fabrice Lacroix
Fabrice Lacroix
cc3e785d5647ada02b76fa329db62950daa3d2d1
Lacroix
Fabrice
Alexander Borgida
d06a067f069937274cca6c2b03a5330b71d660cb
Rutgers University
Borgida
Alexander
Alexander Borgida
Cabot
Senior PC Member at ISWC2012(Research Track)
SAP
SAP
Processing of real-time data streams has become a very important mechanism in many application areas: Smart cities, Smart grid, eHealth, to name but a few. Although semantic technologies have been recognized as one of very important enablers for this type of applications, there are still several challenges to be resolved in order to apply them in real-world applications. Two most important challenges are: an expressive query language that enables the description of complex situations to be detected (considering real-time and historical data) and an efficient asynchronous retrieval mechanism for the large distributed data streams. Beside elaborating on the importance of these challenges for the real-time aware applications, in this tutorial we give an overview of existing approaches for the semantic processing of real-time streams and present a novel approach based on the recent development in two emerging research areas, Complex Event Processing, that enables an efficient in memory processing of huge real-time data and Cloud Computing, that supports an elastic storage of enormous data volumes. Two studies will be demonstrated: We will show conceptual and technical details of the public-available Platform PLAY, that realizes presented approach. We will demonstrate its usage through two applications that combine real-time events coming from Smartphone (geo position, incoming /outgoing /missing calls), Social media (Facebook wall updates and recent tweets) and real-time sensors. The approach for writing semantic adapters for new event sources and semantic patterns to be detected in event streams will be introduced.
2012-11-12T14:00:00+05:00
2012-11-12T17:30:00+05:00
Streams
Scalable semantic processing of huge, distributed real-time streams: Semantics Between Event Processing and Cloud Computing
Razan Paul
9219c0dc7eb89e6e01f45fd3da9da5446e8beec5
University of Queensland
Paul
Razan
Razan Paul
3
Bo Fu
University of Victoria
Fu
Bo
Bo Fu
2
Kendall Clark
1cde8114566a4ec3c99aeee2aab07bebf5cdc768
Clark & Parsia
Clark
Kendall
Kendall Clark
University of Trento
University of Trento
Claus Stadler
University of Leipzig
Stadler
Claus
Claus Stadler
University of Kentucky
University of Kentucky
ISWC2012 Session Chair
University of California, Davis
University of California, Davis
1
Steffen
Steffen Staab
Steffen Staab
ae8f32f31b69df2872d4c5d2e3bf21cf09cadf90
Staab
University of Koblenz and Landau
ISWC2012 Session Chair
Planning
In real world cases, building reliable problem centric views over Linked Data is a challenging task. An ideal method should include a formal representation of the requirements of the needed dataset and a controlled process moving from the original sources to the outcome. We believe that a goal oriented approach, similar to the AI planning problem, could be successful in controlling the process of linked data fusion, as well as to formalize the relations between requirements, process and result.
Linked Data
Planning
Towards a theoretical foundation for the harmonization of linked data
76500434
Linked Data, Data Harmonization, Planning
Linked Data
76500434
Data Harmonization
Towards a theoretical foundation for the harmonization of linked data
Enrico Daga
Data Harmonization
Towards a theoretical foundation for the harmonization of linked data
Jaroslav Kuchar
032105758485339f4f1d530c3b61af5c11b5e3d9
Czech Technical University in Prague
Kuchar
Jaroslav
Jaroslav Kuchar
National and Kapodistrian University of Athens
3d5c1f8e68b6329ae8f6f6b76102c0b33ad72ab5
George Garbis
George Garbis
George
Garbis
Yahoo! Research
Yahoo! Research
ISWC2012 Worshop and Tutorial Chair
Exeter
Stuart
Linked Data have by-and-large been designed around centralized, powerful Web servers and the (mobile) clients accessing them. As a direct consequence of these design decisions, the usage of data- sharing technologies depends on the availability of a Web infrastructure comprised of data-centers, high-speed, reliable Internet connections, and modern client devices. Four-billion people currently have no access to such an infrastructure and are thus deprived of the benefits Linked (Open) Data provides. This tutorial will show how the design principles and technologies of Linked Data can be adapted to distributed networks, and thus contribute to closing this digital data divide. Join us to learn how not to forget the majority of the world population when thinking of the potential users of Linked Data and discuss the challenges this represents for the research community.
ICT4D, Linked Data, Global change, Digital divide
ICT4D
Global change
Linked Data
LD4D: Linked Data for Development
Digital divide
Linked Data
Global change
ICT4D
Digital divide
2012-11-12T17:30:00+05:00
2012-11-12T09:00:00+05:00
LD4D
Discoverability
Discoverability,Open Educational Resources,Linked Data,Recommandation,Named Entity Recognition,Application,Summarisation
Summarisation
DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries
We demonstrate the DiscOU engine implementing a resource discovery approach where the textual components of open educational resources are automatically annotated with relevant entities (using a named entity recognition system), so that these rich annotations can be searched by similarity, based on existing resources of interest.
Open Educational Resources
Named Entity Recognition
Linked Data
Recommandation
Summarisation
Discoverability
Recommandation
DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries
Application
Open Educational Resources
Linked Data
Mathieu d'Aquin, Carlo Allocca and Trevor Collins
Named Entity Recognition
Application
DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries
Hypios
Hypios
Free University of Bozen-Bolzano
Mariano
Rodriguez-Muro
Mariano Rodriguez-Muro
9834233dbc9d00654ac36e085f897a38c1a88df3
Mariano Rodriguez-Muro
3
Franconi
cc31df1716defe7e7094b4e019d2218078072e53
Enrico
Enrico Franconi
Free University of Bozen-Bolzano
Enrico Franconi
Chris Chaulk
5b51949c829f9c0d7405e7e19a79fd56dc65ef49
EMC Corporation
Chaulk
Chris
Chris Chaulk
Tim Clark
d9c9efcbcb2877d085cff13ec8b748772fec0ed6
Massachusetts General Hospital
Clark
Tim
Tim Clark
2183f513ec0eec352b179b9dfa131c7a37b945ad
Image & Pervasive Access Lab (IPAL), UMI CNRS
Thibaut
Tiberghien
Thibaut Tiberghien
Thibaut Tiberghien
University of Grenoble
University of Grenoble
Martin Theobald
6946bbceaf6bac12a8e5029301ef7eac3c221839
Max Planck Institute for Informatics
Theobald
Martin
Martin Theobald
Hans Vasquez-Gross
414d78cf3661498dab056a741c8aa5b8f61e79c2
University of California, Davis
Vasquez-Gross
Hans
Hans Vasquez-Gross
Magnus Knuth
2f6f063a903efc8fa6c05e51d51ae30bb82ab385
Hasso Plattner Institute
Knuth
Magnus
Magnus Knuth
University of Zurich
Abraham Bernstein
Abraham
Bernstein
Abraham Bernstein
Richard Cyganiak
DERI, NUI Galway
Cyganiak
Richard
Richard Cyganiak
1
Stanford University
Stanford University
3
Manuel Atencia
1a955294e7f96dc652cb0622b88d256b8765bda7
INRIA & University of Grenoble
Atencia
Manuel
Manuel Atencia
DERI, NUI Galway
Vit Novacek
Novacek
Vit
c91b8f5b6908408264728371f7331249e3857a87
Vit Novacek
Linked Data
Data publication
Licensing,Linked Data,Data publication
Serena Villata and Fabien Gandon
Towards Licenses Compatibility and Composition in the Web of Data
We propose a general framework to attach the licensing terms to the data where the compatibility of the licensing terms concerning the data affected by a query is verified, and, if compatible, the licenses are combined into a composite license. The framework returns the composite license as licensing term about the data resulting from the query.
Linked Data
Licensing
Towards Licenses Compatibility and Composition in the Web of Data
Data publication
Licensing
Towards Licenses Compatibility and Composition in the Web of Data
Universitat de Lleida
Universitat de Lleida
Jie Liu
88b1a16c736d1117acebcfb8035cba651d54f3c8
UALR
Liu
Jie
Jie Liu
Justifications
Justifications
76500282
Justification Finding
Extracting Justifications from BioPortal Ontologies
Justification Finding
Blackbox Debugging
Matthew Horridge, Bijan Parsia and Ulrike Sattler
This paper presents an evaluation of state of the art black box justification finding algorithms on the NCBO BioPortal ontology corpus. This corpus represents a set of naturally occurring ontologies that vary greatly in size and expressivity. The results paint a picture of the performance that can be expected when finding all justifications for entailments using black box justification finding techniques. The results also show that many naturally occurring ontologies exhibit a rich justificatory structure, with some ontologies having extremely high numbers of justifications per entailment.
Justifications,Justification Finding,Explanation,Blackbox Debugging
Extracting Justifications from BioPortal Ontologies
Extracting Justifications from BioPortal Ontologies
Blackbox Debugging
Explanation
76500282
Explanation
76500446
76500446
Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data
A pair of RDF instances are said to corefer when they are intended to denote the same thing in the world, for example, when two nodes of type foaf:Person describe the same individual. This problem is central to integrating and inter-linking semi-structured datasets. We are developing an online, unsupervised coreference resolution framework for heterogeneous, semi-structured data. The online aspect requires us to process new instances as they appear and not as a batch. The instances are heterogeneous in that they may contain terms from different ontologies whose alignments are not known in advance. Our framework encompasses a two-phased clustering algorithm that is both flexible and distributable, a probabilistic multidimensional attribute model that will support robust schema mappings, and a consolidation algorithm that will be used to perform instance consolidation in order to improve accuracy rates over time by addressing data sparseness.
Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data
Jennifer Sleeman
Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data
1
University of Bologna
University of Bologna
Fausto Guinchiliglia
University of Trento
Guinchiliglia
Fausto
Fausto Guinchiliglia
Andreas Thalhammer
6f575397b076c84395e15262ac5339f249ad0fb4
University of Innsbruck
Thalhammer
Andreas
Andreas Thalhammer
Stavropoulos
Thanos G. Stavropoulos
Aristotle University of Thessaloniki
d1d69c3d1cc349f98825dde21d315ddb8c11fabe
Thanos G.
Thanos G. Stavropoulos
Fuzzy ontologies
Uncertainty reasoning
8th International Workshop on Uncertainty Reasoning for the Semantic Web
Uncertainty representation
Fuzzy ontologies
Imperfect knowledge
2012-11-11T17:30:00+05:00
Probabilistic ontologies
Imperfect knowledge
Uncertainty reasoning, Uncertainty representation, Imperfect knowledge, Probabilistic ontologies, Fuzzy ontologies
Probabilistic ontologies
Uncertainty representation
URSW
Uncertainty reasoning
2012-11-11T09:00:00+05:00
This workshop will discuss different approach to deal with uncertainty (in a wide sense) in the Semantic Web. Uncertainty is an unavoidable factor in several processes, such as knowledge interchange and application interoperability, and Semantic Web data, which are usually incomplete, inconsistent, and inaccurate. This suggests the need to apply different formalisms (probability, fuzzy logic, decision theory, etc.) to enrich Semantic Web technologies and applications.
Bioinformatics at Centre for Plant Biotechnology and Genomics UPM-INIA
Bioinformatics at Centre for Plant Biotechnology and Genomics UPM-INIA
University of Leipzig
University of Leipzig
Mikko Rinne
Complex event processing
SPARQL Update for Complex Event Processing
SPARQL Update for Complex Event Processing
SPARQL
Complex event processing
SPARQL
RDF
Complex event processing, SPARQL, RDF, Rete-algorithm
Rete-algorithm
Rete-algorithm
76500442
SPARQL Update for Complex Event Processing
RDF
Complex event processing is currently done primarily with proprietary definition languages. Future smart environments will require collaboration of multi-platform sensors operated by multiple parties. The goal of my research is to verify the applicability of standard-compliant SPARQL for complex event processing tasks. If successful, semantic web standards RDF, SPARQL and OWL with their established base of tools have many other benefits for event processing including support for interconnecting disjoint vocabularies, enriching event information with linked open data and reasoning over semantically annotated content. A software platform capable of continuous incremental evaluation of multiple parallel SPARQL queries is a key enabler of the approach.
76500442
Leigh Dodds
1bca73e5c6916c738d6ec7cc0597ad0e395e7ace
Ingenta
Dodds
Leigh
Leigh Dodds
Information Sciences Institute, University of Southern California
Information Sciences Institute, University of Southern California
Université Libre de Bruxelles
Université Libre de Bruxelles
AKSW
AKSW
Lina Zhou
University of Maryland Baltimore County
Zhou
Lina
Lina Zhou
Josef Hardi
04f2b90522cae30f692de284a130cea1762a409c
Free University of Bozen-Bolzano
Hardi
Josef
Josef Hardi
001e4d4a00b9bf4295967dcc0d3840c147704c63
Alessio Palmero Aprosio
Fondazione Bruno Kessler
Alessio
Palmero Aprosio
Alessio Palmero Aprosio
1
2
Yongchun Xu
5ac35e64c1fb85d5d1442a6013cedf6187a623df
FZI Research Center for Information Technology
Xu
Yongchun
Yongchun Xu
Osaka University
Osaka University
Rete
INSTANS: High-Performance Event Processing with Standard RDF and SPARQL
Rete,SPARQL,RDF,Complex event processing
INSTANS: High-Performance Event Processing with Standard RDF and SPARQL
SPARQL
Complex event processing
Smart environments require collaboration of multi-platform sensors operated by multiple parties. Proprietary event processing solutions do not have enough interoperation flexibility, easily leading to overlapping functions wasting hardware and software resources as well as data communications. Our goal is to verify the applicability of standard-compliant SPARQL for any complex event processing task. If found feasible, semantic web methods RDF, SPARQL and OWL have the built-in support for interconnecting disjoint vocabularies, enriching event information with linked open data and reasoning over semantically annotated content, yielding a very flexible event processing environment. Our approach is designed to meet these requirements. Our INSTANS platform based on continuous execution of interconnected SPARQL queries using the Rete-algorithm is a new approach showing improved performance for event processing tasks over current SPARQL-based solutions.
SPARQL
Mikko Rinne, Esko Nuutila and Seppo Törmä
INSTANS: High-Performance Event Processing with Standard RDF and SPARQL
RDF
Complex event processing
RDF
Rete
Miranker
Daniel
University of Texas at Austin
Daniel Miranker
0fea94336fe97522ad08d3100f2555d8b1acab05
Daniel Miranker
Orri Erling
2d8d880652d4a0c0beba9ea1f96eab72ba83b10d
OpenLink
Erling
Orri
Orri Erling
ISWC2012 Session Mediator
Bielefeld University
Bielefeld University
Serena Villata
200fa053a13df35295f805b07e244b0922bb0010
INRIA
Villata
Serena
Serena Villata
2
Hendler
Jim Hendler
Jim
Jim Hendler
b03a8c07c75cce97ff800d07844b75fd3a12f9ae
RPI
Annika Hinze
8db2a25da9254fe29f683ecf7ca66f5111684604
University of Waikato
Hinze
Annika
Annika Hinze
James Fan
0212761867f0f75b22330290eab1c76576fa3a97
IBM Research
Fan
James
James Fan
OpenLink
OpenLink
Sandia National Laboratories
Sandia National Laboratories
1
Kemafor Anyanwu
Anyanwu
Kemafor Anyanwu
North Carolina State University
Kemafor
8caa6f23c7e9f622ab0b047a4850f1b32a505ae0
Kai-Uwe Sattler
5a074b484cb48f9056d70c9d136a3c6a296346e0
TU Ilmenau
Sattler
Kai-Uwe
Kai-Uwe Sattler
3
John Yu
eec594a4f3e4a9cf28a0a21f6bca8f4b90108cd0
University of California, Davis
Yu
John
John Yu
Carlo Curino
Yahoo! Research
Curino
Carlo
Carlo Curino
71a761b4b91daced90923b5fc2d37f7afeb2c501
Nuzzolese
University of Bologna;STLab, ISTC-CNR
Andrea
Andrea Giovanni Nuzzolese
Andrea Giovanni Nuzzolese
ISWC2012 Session Chair
Papoutsis
2633e011aa5f6af7d07067425939fdcb7ca08620
Ioannis Papoutsis
Ioannis
Ioannis Papoutsis
National Observatory of Athens
ISWC2012 Session Chair
Data
Platform
SPUD: Semantic Processing of Urban Data
Urban
Integration
Diagnosis
We present SPUD, a semantic environment for cataloguing, exploring, integrating, understanding, processing and transforming urban information. Such information comes from the Web, government authorities, social networks and Linked Data sources. We identify a scenario consisting of the following cases: (a) Publication of a dataset, focusing on privacy protection and semantic annotation of the dataset, (b) Reporting and Consolidation of multi-faceted information, focusing on searching and visualizing heterogenous data from several sources, including social media, linked data and government data, (c) Diagnosis, based on highly expressive reasoning. Through our demonstration, we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogenous real datasets coming from different data sources and spanning multiple domains.
Diagnosis
Integration
Urban
Data
Semantic
Semantic
Urban,Semantic,Integration,Diagnosis,Platform,Data
Spyros Kotoulas, Vanessa Lopez, Raymond Lloyd, Marco Luca Sbodio, Freddy Lecue, Martin Stephenson, Elizabeth Daly, Veli Bicer, Aris Gkoulalas-Divanis, Giusy Di Lorenzo, Anika Schumann and Pol Mac Aonghusa
Platform
SPUD: Semantic Processing of Urban Data
SPUD: Semantic Processing of Urban Data
Kathryn Laskey
Kathryn Laskey
Kathryn
80300d4d62a9c1e49456c5406fd4746d6f73ad35
George Mason University
Laskey
3
Marine Biological Laboratory
Marine Biological Laboratory
entity summarization
76500342
entity summarization,property ranking,evaluation,linked data,quiz game,games with a purpose
Evaluating Entity Summarization Using a Game-Based Ground Truth
Andreas Thalhammer, Magnus Knuth and Harald Sack
In recent years, strategies for Linked Data consumption have caught attention in Semantic Web research. For direct consumption by users, Linked Data mashups, interfaces, and visualizations have become a popular research area. Many approaches in this field aim to make Linked Data interaction more user friendly to improve its accessibility for nontechnical users. A subtask for Linked Data interfaces is to present entities and their properties in a concise form. In general, these summaries take individual attributes and sometimes user contexts and preferences into account. But the objective evaluation of the quality of such summaries is an expensive task. In this paper we introduce a game-based approach aiming to establish a ground truth for the evaluation of entity summarization. We exemplify the applicability of the approach by evaluating two recent summarization approaches.
Evaluating Entity Summarization Using a Game-Based Ground Truth
76500342
property ranking
entity summarization
linked data
evaluation
games with a purpose
linked data
games with a purpose
evaluation
quiz game
Evaluating Entity Summarization Using a Game-Based Ground Truth
quiz game
property ranking
2
Christian Dirschl
e69284bd9535f3f24d901076e71d135abb3cd0ab
Wolters Kluwer
Dirschl
Christian
Christian Dirschl
Wim
University of Sheffield
Peters
360541873bc3c704abe6ed9f1a3ce13180e93740
Wim Peters
Wim Peters
Steven Jennings
UALR
Jennings
Steven
Steven Jennings
Joanne Luciano
e7660b9a608c0929b12ac5466afc5dec90fbdae5
RPI
Luciano
Joanne
Joanne Luciano
VU Amsterdam
Laura Hollink
Laura
d6541facb723c7ec3e0df552b89a7aeb99321bb8
Laura Hollink
Hollink
The Trials and Tribulations of a Semantic Technology Evangelist
2012-11-15T01:00:00+05:00
2012-11-14T19:30:00+05:00
This talk will attempt to be a humorous reprise of attempts to realise the web of Open Linked Data. What have been the great successes and failures and what does the future hold? Will the empire strike back? All will be revealed in the banquet after Dinner speech.
2
Monika Solanki
006555fcc637d698a751a72c6f062a91c2fdef3b
Birmingham City University
Solanki
Monika
Monika Solanki
Héctor Pérez-Urbina
a6f7b3b72fbfa8608edf6e5835bb80c5e6cad7e3
Clark & Parsia
Pérez-Urbina
Héctor
Héctor Pérez-Urbina
University Carlos III of Madrid
University Carlos III of Madrid
Cambridge
streaming linked data
Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony
twitter
stream reasoning
real time analysis
stream reasoning
Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony
crowd attention
stream reasoning,real time analysis,crowd movements,crowd attention,csparql,streaming linked data,linked data,twitter,social media,social media analysis,london2012 olympic games
social media
streaming linked data
london2012 olympic games
twitter
To manage a big event require tracking in real time the movement of crowds. Solution based on mobile network data analysis are effective, but expensive. Obtaining comparable results by analysing public social stream has a clear commercial potential, especially considering that, being able to access also the content of a micro-post, the analysis can also track the attention of crowds.
social media
Marco Balduini and Emanuele Della Valle
social media analysis
csparql
crowd movements
crowd attention
linked data
real time analysis
csparql
linked data
social media analysis
london2012 olympic games
Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony
crowd movements
Takahisa Fujino
1bfafa87e50ab93db7527ea7b7943751d0665ce8
Shizuoka University
Fujino
Takahisa
Takahisa Fujino
EMC Corporation
EMC Corporation
ISWC2012 Proceedings Chair
We present QAKiS, a system for open domain Question Answering over linked data. It addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. For the demo, the relational patterns are automatically extracted from Wikipedia, while DBpedia is the RDF data set to be queried using a natural language interface.
Question Answering,Natural Language Processing,Linked Data,BDpedia
BDpedia
Elena Cabrio, Julien Cojan, Alessio Palmero Aprosio, Bernardo Magnini, Alberto Lavelli and Fabien Gandon
Linked Data
Question Answering
BDpedia
Linked Data
QAKiS: an Open Domain QA System based on Relational Patterns
QAKiS: an Open Domain QA System based on Relational Patterns
QAKiS: an Open Domain QA System based on Relational Patterns
Natural Language Processing
Natural Language Processing
Question Answering
1
Sudeshna Das
357738dae0683e91b90de98b513d8b24c0baa71e
Massachusetts General Hospital
Das
Sudeshna
Sudeshna Das
2
3
DeRiVE-2012
2012-11-12T14:00:00+05:00
event exploitation
social media
multimedia
The goal of this workshop is to strengthen the participation of the semantic web community in the recent surge of research on the use of events as a key concept for representing knowledge and organising and structuring media on the web. The workshop invites contributions to three central themes: event detection, event representation, and event exploitation. Its goal is to formulate answers to questions in these themes that advance and reflect the current state of understanding. Each submission will be expected to address at least two themes explicitly, if possible including a system demonstration. This year, we specifically invite contributions that address both event and conversation semantics in multimedia and social media.
2012-11-12T17:30:00+05:00
event representation
event detection
event detection
event representation
event exploitation
Detection, Representation, and Exploitation of Events in the Semantic Web
social media
multimedia
event detection, event representation, event exploitation, multimedia, social media
Thomas
Bouttaz
University of Aberdeen
Thomas Bouttaz
Thomas Bouttaz
d1cf7d8edb6c1552e3699683c6d3a90fccfcb8dc
Composition of Linked Data-based RESTful Services
76500450
76500450
Composition of Linked Data-based RESTful Services
Steffen Stadtmüller
We address the problem of developing a scalable composition framework for Linked Data-based services, which retains the advantages of the loose coupling fostered by REST.
Composition of Linked Data-based RESTful Services
University of Mannheim
University of Mannheim
EXALEAD, Dassault Systèmes
EXALEAD, Dassault Systèmes
Institute for Infocomm Research
Institute for Infocomm Research
Francesco Osborne
University of Turin
Francesco Osborne
651c4a4af5ab4db99ecc207b495be0cc351e86e8
Osborne
Francesco
Daniel Schwabe
PUC-Rio
Schwabe
Daniel
Daniel Schwabe
Julien Law-To
ffbd27758de83c14456fe7a7cb238ab6f2c2f22f
EXALEAD, Dassault Systèmes
Law-To
Julien
Julien Law-To
University of Bari
ab740dad9a301ee0435c0d8187a4e2d905165aa7
de Gemmis
Marco de Gemmis
Marco de Gemmis
Marco
PC Member at ISWC2012(Semantic Web In Use Track)
Thorsten Krueger
0f9a07ed9da2425024e704ebb8e241f45a31f33f
Siemens AG
Krueger
Thorsten
Thorsten Krueger
2012-11-14T19:30:00+05:00
2012-11-14T09:00:00+05:00
Registration
Seema Sundara
6a8c49e2c1c8bcde071dde92d5377b32034794a3
Oracle
Sundara
Seema
Seema Sundara
Workshop Chair
David Norheim
David
David Norheim
Norheim
31c2448be0c03734932b4919b7278a4133a70f29
Computas
a69a534c7a5802053569792e8c769d8981487eb4
Josiane
Josiane Xavier Parreira
DERI, NUI Galway
Parreira
Josiane Xavier Parreira
2bca83b0b9ef7088e67a77a8c749f97b6e072cc6
Aston University
Brewster
Christopher
Christopher Brewster
Christopher Brewster
University of Bologna
University of Bologna
Telecom Bretagne
Telecom Bretagne
Humboldt University of Berlin
Humboldt University of Berlin
Brian Kettler
Lockheed Martin
Kettler
Brian
Brian Kettler
Workshop Chair
Corcho
Universidad Politecnica de Madrid
efbd90eca236ae3131e67f30e3abe0a1bceff305
Oscar
Oscar Corcho
87476c0ef395e4ba818199fb0c1101e4d5811c2a
Oscar Corcho
Charalambos Kontoes
8c9f34c7ae4eaf63c5b0ba473eae819206215c9a
National Observatory of Athens
Kontoes
Charalambos
Charalambos Kontoes
University of Oxford
Markus Krötzsch
Markus Krötzsch
Markus
Krötzsch
9d431a8ef18a887f519056a688c86b2d5099a188
2012-11-13T21:00:00+05:00
2012-11-13T08:30:00+05:00
Registration
643f7225974f4090aa9a7d0b3ba82e54736970eb
Kapila Ponnamperuma
Kapila Ponnamperuma
Kapila
Ponnamperuma
University of Aberdeen
Christophe Guéret
VU Amsterdam
Guéret
Christophe
Christophe Guéret
Chenghua Lin
48ca4065e4efc565181350eee98a176a67d7e0db
KMi, The Open University
Lin
Chenghua
Chenghua Lin
Concordia University
Concordia University
274ee5c2434a2eb368dce6c789179aec8eb148d3
James
James Pustejovsky
Pustejovsky
Brandeis University
James Pustejovsky
Maria-Esther
Vidal
Universidad Simon Bolivar
Maria-Esther Vidal
6c9651932804d271e53f279c99d7cd1d1d429c0a
Maria-Esther Vidal
Workshop Chair
Knowledge Modelling
Inference Engine
Knowledge Modelling
Context Awareness
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia
Context Awareness
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia
Inference Engine
76500209
Ambient Assisted Living
Semantic Web
Semantic Web
Ambient Assisted Living,Context Awareness,Knowledge Modelling,Semantic Web,Inference Engine
Ambient Assisted Living
Thibaut Tiberghien, Hamdi Aloulou, Mounir Mokhtari and Jit Biswas
Robust solutions for ambient assisted living are numerous, yet predominantly specific in their scope of usability. In this paper, we describe the potential contribution of semantic web technologies to building more versatile solutions - a step towards adaptable context-aware engines and simplified deployments. Our conception and deployment work in hindsight, we highlight some implementation challenges and requirements for semantic web tools that would help to ease the development of context-aware services and thus generalize real-life deployment of semantically driven assistive technologies. We also compare available tools with regard to these requirements and validate our choices by providing some results from a real-life deployment.
76500209
Horridge
Matthew Horridge
29c0127871609390f3d73c72c9418cc2936ac0f8
Stanford University
Matthew Horridge
Matthew
Computas
Computas
KMi, The Open University
Carlos
Carlos Pedrinaci
7dd3dfdb407a46327824fc54ebedcc29caf1c553
Pedrinaci
Carlos Pedrinaci
bb20db8f12d41be62a42151b280f121194e95179
Enrico
Motta
Enrico Motta
KMi, The Open University
Enrico Motta
28a0f82609671f47d811e6bee865afb23abfb8db
Jennifer Golbeck
University of Maryland
Golbeck
Jennifer
Jennifer Golbeck
Xing Niu
849b1c5ff985c3098cefda9279b6c1150ed3fd32
Shanghai Jiao Tong University
Niu
Xing
Xing Niu
David Newman
ebfe5ff627066f7cd366d7d08de3e8def9c0ec1f
Wells Fargo Bank
Newman
David
David Newman
University of Pittstburgh
University of Pittstburgh
Stadtmüller
Steffen Stadtmüller
547b31bc0d6de8e1cc9256b9ec77224fc28157f3
Steffen Stadtmüller
Steffen
AIFB, Karlsruhe Institute of Technology
University of Victoria
University of Victoria
7727b0b71e1bbd02c6fafb981ac417c77b0eb51d
University of Oxford
Thomas
Thomas Lukasiewicz
Thomas Lukasiewicz
Lukasiewicz
ea223fd1ac3921cfb6e7e515e6cfc22050b9146c
In-Use Track
UAMS
UAMS
Big Graph Data Panel
2012-11-14T14:00:00+05:00
2012-11-14T15:30:00+05:00
The Semantic Web / Linked Data has grown immensely over the past years. When the Semantic Web community started working over a decade ago the main question was where to get the data from. By now the question of how to process ever increasing amount of semantic/linked data has come to people's utmost attention. The goal of this panel is to shed light on the various approaches/options for Big Graph Data processing. Possible questions include: (1)Does the Semantic Web need any central infrastructures? (It's a Web, after all?) (2)Or will a handful of large single-owner infrastructures dominate the Semantic Web, just as they now dominate the current Web? (3)And if so, will such infrastructures be based on the standard relational model? (4)Or on MapReduce-centric key/value-pairs? (5)Is Google's (centralised) Knowledge Graph anathema to the Semantic *Web* ? (6)Are triplestore vendors just reinventing the old database wheels? (7)What is the role of clustered MapReduce-like solutions and where are their limits for processing semantic web data?
Nenad
Nenad Stojanovic
53a4bdea8bebaa7af9c3d002dc19b0fd88c7efdb
Stojanovic
Nenad Stojanovic
FZI Research Center for Information Technology
2012-11-13T11:00:00+05:00
2012-11-13T10:30:00+05:00
Coffee Break
Simperl
AIFB, Karlsruhe Institute of Technology
cb10de0e282d7010d2b7326b8c34bb59e4d3f9fa
Elena
Elena Simperl
Elena Simperl
Watson
Answer Typing
A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson
A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson
76500240
Question Answering
Question Answering,Answer Typing,Watson
76500240
Questions often explicitly request a particular type of answer. One popular approach to answering natural language questions involves filtering candidate answers based on precompiled lists of instances of common answer types (e.g., countries, animals, foods, etc.). Such a strategy is poorly suited to an open domain in which there is an extremely broad range of types of answers, and the most frequently occurring types cover only a small fraction of all answers. In this paper we present an alternative approach called TyCor, that employs soft filtering of candidates using multiple strategies and sources. We find that TyCor significantly outperforms a single-source, single-strategy hard filtering approach, demonstrating both that multi-source multi-strategy outperforms a single source, single strategy, and that its fault tolerance yields significantly better performance than a hard filter.
Watson
Answer Typing
A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson
Question Answering
Chris Welty, J William Murdock, Aditya Kalyanpur and James Fan
1
Andreas Thor
0ed5bf33ce4bf0c34dc7f2846b17ac4c28e3d519
University of Leipzig
Thor
Andreas
Andreas Thor
3
Guillermo Palma
ec8c21a30b57df2d5a8b55e8e3a0723ec3446bd0
Universidad Simon Bolivar
Palma
Guillermo
Guillermo Palma
2
Cynthia Chang
7aff73c97fdc96b65af1059d4c977fc59eb71e36
RPI
Chang
Cynthia
Cynthia Chang
Vinay Chaudhri
f0bbfc648c07c071dbea8609a6db246864cc225c
SRI International
Chaudhri
Vinay
Vinay Chaudhri
ae1e85c676d68fdca4124f2316c1c800edde6c2f
Thetida Zetta
Zetta
Aristotle University of Thessaloniki;Cardiff University
Thetida Zetta
Thetida
Nigel Shadbolt
University of Southampton
Nigel Shadbolt, Professor of Artificial Intelligence and Head of the Web and Internet Science Group at the University of Southampton has been confirmed as the dinner speaker. He is a Director of the Web Science Trust, and of the Web Foundation - both organisations have a common commitment to advance our understanding of the Web and promote the Webs positive impact on society. In June 2009 together with Sir Tim Berners-Lee he was appointed an Information Advisor by the Prime Minister to help transform public access to Government information. A major output of this work has been the widely acclaimed data.gov.uk site - a single point of access for all Government non-personal public data. In May 2010 he was appointed by the Coalition Government to the Public Sector Transparency Board responsible for setting open data standards across the public sector and developing the legal Right to Data. He also chairs the Local Data Panel seeking to promote and develop open data approaches within Local Government.
Shadbolt
Nigel
Nigel Shadbolt
Lorenz Bühmann
af15b119f30b750e47a5b8658ee5dee630219263
Lorenz Bühmann
University of Leipzig
Lorenz
Bühmann
Spiros Skiadopoulos
University of Peloponnese
Skiadopoulos
Spiros
Spiros Skiadopoulos
George Mason University
9ee90784167a09162a38823d4627a53700513d3f
Paulo
Costa
Paulo Costa
Paulo Costa
Hima Yalamanchili
944321034d9be8f579a352fd674e6a9354bfda62
Kno.e.sis Center, Wright State University
Yalamanchili
Hima
Hima Yalamanchili
2012-11-13T16:00:00+05:00
2012-11-13T15:30:00+05:00
Coffee Break
Hanmin Jung
5c0260fcfaaaaa4144abe3272262b847c660f0c2
Korea Institute of Science and Technology Information
Jung
Hanmin
Hanmin Jung
Julien Cojan
495815152a22a8af9ddcb916ad8986de667a76e7
INRIA
Cojan
Julien
Julien Cojan
Jacobs University
Jacobs University
1
Beacon Hill
Free University of Bozen-Bolzano
Free University of Bozen-Bolzano
TU Darmstadt
TU Darmstadt
Nitish Aggarwal
9b85e236d9f0aca98718221823309786cc2bc9d5
DERI, NUI Galway
Aggarwal
Nitish
Nitish Aggarwal
Universidad de Chile
f341588c8c974d3b6fa4134e12700da871e8704f
Gutierrez
Claudio Gutierrez
Claudio Gutierrez
998f9b4f01cf1bc5f74b60b15a755e4dcde30d76
Claudio
University of Waikato
University of Waikato
1
Stanford University
Manuel Salvadores
Manuel
Salvadores
e9278f3dd37dcb1f98d2f9184bab77d90228f858
Manuel Salvadores
92ea611cf55f95a0ffd94eca818bb9d8a3f9a735
Manolis Koubarakis
Manolis Koubarakis
Manolis
National and Kapodistrian University of Athens
Koubarakis
Paul Alexander
e4532edb374cb842d2bf282fc686bcb43b79833c
Stanford University
Alexander
Paul
Paul Alexander
McNeill
University of Edinburgh
Fiona
Fiona McNeill
Fiona McNeill
38bbf1be818195da9679db35d8604b007404b542
CRIM
CRIM
Georgian
University of Applied Sciences and Arts Northwestern Switzerland
University of Applied Sciences and Arts Northwestern Switzerland
University of Southern California
University of Southern California
Chris Baillie
6fb5a96a446e73369ffeff680a10a3988cdd3778
University of Aberdeen
Baillie
Chris
Chris Baillie
Data.gov, U.S. General Services Administration
Jeanne Holm
As the Evangelist for Data.Gov (an open government flagship project for the White House managed by GSA), Jeanne Holm leads collaboration and builds communities with the public, educators, developers, and international and state governments in using open government data. Jeanne is the Chief Knowledge Architect at NASAs Jet Propulsion Laboratory, driving innovation through social media, virtual worlds, gaming, ontologies, and collaborative systems, including the award-winning NASA public portal (www.nasa.gov) and pioneering knowledge architectures within DoD. She is a Fellow of the United Nations International Academy of Astronautics and a Distinguished Instructor at UCLA, with more than 130 publications on information systems, knowledge management, and innovation.
5c8f5d03a924ab105d1dfd3cf1a7d0e66269f7f1
Jeanne
Holm
Jeanne Holm
Paulo Pinheiro Da Silva
University of Texas at El Paso
Silva
Paulo
Paulo Pinheiro Da Silva
PES Institute of Technology
PES Institute of Technology
Pavlos
FORTH-ICS
Pavlos Fafalios
Fafalios
e7d434b999fef7138cbe6610e4e6f0f787336c1b
Pavlos Fafalios
2012-11-14T14:00:00+05:00
2012-11-14T12:30:00+05:00
Mentor Lunch
Haase
Peter
Peter Haase
Peter Haase
fluid Operations
fec12b76b11eceb8dce87a648d055583e575d0c6
Stephenson
90498c471a6ce3976f8c5413c586e5dab69a2a20
Martin
Martin Stephenson
Martin Stephenson
IBM Research
Ian Davis
89f6d4dd2fb06a92b4bba8827b52a06fe2babb76
self
Davis
Ian
Ian Davis
Sergio Tessaris
Free University of Bozen-Bolzano
Tessaris
Sergio
Sergio Tessaris
Massimo Paolucci
399afe97d2817172bb4122357cc622fc558ccd48
DoCoMo Euro labs
Paolucci
Massimo
Massimo Paolucci
Karin Breitman
5e768fc4a7f130549b112247be2d54de8e8d1c5d
EMC R&D Brazil
Breitman
Karin
Karin Breitman
University of Applied Sciences Western Switzerland
University of Applied Sciences Western Switzerland
semantic sensor networks
sensor data
ssn
sensor web
5th International Workshop on Semantic Sensor Networks
2012-11-12T09:00:00+05:00
The workshop aims to provide an inter-disciplinary forum to explore and promote the technologies related to a combination of semantic web and sensor networking. Specifically, to develop an understanding of the ways semantic web technologies can contribute to the growth, application and deployment of large-scale sensor networks on the one hand, and the ways that sensor networks can contribute to the emerging semantic web, on the other.
SWE
sensor data
semantic sensor networks
sensor web
2012-11-12T17:30:00+05:00
SWE
semantic sensor networks, ssn, sensor web, SWE, sensor data
SSN
ssn
ISWC2012 Industry Chair
Marco Luca Sbodio
Marco Luca Sbodio
577eb4f75845c4576b906544c29f5b4a729fb465
IBM Research
Sbodio
Marco Luca
ISWC2012 Sponsorship Chair
Giovanni Semeraro
University of Bari
Giovanni
Semeraro
Giovanni Semeraro
800698093350bcbb94beb80799c9e84dc7e0e9c4
York Sure-Vetter
York
GESIS - Leibniz Institute for the Social Sciences; University of Koblenz and Landau
0969bcedb4a9a13e8399506967a744d2dd11ebda
Sure-Vetter
York Sure-Vetter
Nicola
3c1395bd919491feee209479341d32552e97231c
Nicola Fanizzi
Nicola Fanizzi
Fanizzi
University of Bari
Workshop Chair
3
2012-11-10T17:00:00+05:00
2012-11-10T09:00:00+05:00
The world is changing fast and so is Elsevier. To build and deploy future research tools we need to seriously collaborate in a linked and distributed environment. Building on earlier developer events such as Executable Paper Challenge, Life Sciences Challenge and Apps for Science, Elsevier and the 11th International Semantic Web Conference are co-hosting CodeForScience Boston, a competition for building a tool that best extends how scientific researchers search, process, integrate and share. The application concept formulation and submission will take place online, followed by a live coding day on Saturday, November 10th at MIT Stata Center in Cambridge, MA. Prizes will be awarded. Registration for CodeForScience Boston will open in early October.
Code for Science Linked Datathon
Ghent University
Van Deursen
20ce3b0262f51732189cf73813162ecc5c98cab9
Davy Van Deursen
Davy Van Deursen
Davy
Workshop Chair
Centrum Wiskunde & Informatica (CWI)
Centrum Wiskunde & Informatica (CWI)
Opher Etzion
713e1a26f5e8e6d3feaf55a8da75fd7a4556b8b3
IBM Research Lab , Haifa, Israel
Etzion
Opher
Opher Etzion
Reinvent Technology
Reinvent Technology
Esko Nuutila
54ea9acbfd1a086718c0b100e91eef0621683f25
Aalto University
Nuutila
Esko
Esko Nuutila
Andreas
5660b83b50341a585b7b38ffeafe0163142338ba
Andreas Zankl
Andreas Zankl
University of Queensland
Zankl
Yannis Kalfoglou
self
Kalfoglou
Yannis
Yannis Kalfoglou
Jönköping University
Karl
Karl Hammar
Karl Hammar
Hammar
8b3d5c5936c7907471dcdda70f09ccff222498be
Vinh Nguyen
Kno.e.sis Center, Wright State University
Nguyen
Vinh Nguyen
Vinh
a59985628718c1893d88b887589ccfeec889f6d1
3roundstones
3roundstones
Raúl García-Castro
907b89a2b177f2730c2d6ab5c855331fb434d497
Universidad Politecnica de Madrid
García-Castro
Raúl
Raúl García-Castro
Craig Knoblock
2c2715555efac793759255fe12d117541cf52a37
Information Sciences Institute, University of Southern California
Knoblock
Craig Knoblock
Craig
National Technical University of Athens
National Technical University of Athens
2012-11-14T12:30:00+05:00
Industry Track I
2012-11-14T11:00:00+05:00
Pieterjan De Potter
95508860a6e76ad872c5bbe9c98ba681be97098d
Ghent University
De Potter
Pieterjan
Pieterjan De Potter
2012-11-14T17:30:00+05:00
2012-11-14T16:00:00+05:00
Streaming and Geospatial DBMSs
Rafael S. Gonçalves
051af5c11e8f64bc668957e15b9a3ea541a386b2
University of Manchester
Rafael S. Gonçalves
Rafael
Gonçalves
Ontology Classification
Ana Armas, Bernardo Cuenca Grau and Ian Horrocks
MORe: Modular Combination of OWL Reasoners for Ontology Classification
Modularity
76490001
Ontology Classification
Modularity
MORe: Modular Combination of OWL Reasoners for Ontology Classification
Ontologies
Automated reasoning
76490001
Ontologies
Ontologies,Ontology Classification,Automated reasoning,Modularity,Semantic Web
Semantic Web
MORe: Modular Combination of OWL Reasoners for Ontology Classification
Classification is a fundamental reasoning task in ontology design, and there is currently a wide range of reasoners highly optimised for classification of OWL 2 ontologies. There are also several reasoners that are complete for restricted fragments of OWL 2 , such as the OWL 2 EL profile. These reasoners are much more efficient than fully-fledged OWL 2 reasoners, but they are not complete for ontologies containing (even if just a few) axioms outside the relevant fragment. In this paper, we propose a novel classification technique that combines an OWL 2 reasoner and an efficient reasoner for a given fragment in such a way that the bulk of the workload is assigned to the latter. Reasoners are combined in a black-box modular manner, and the specifics of their implementation (and even of their reasoning technique) are irrelevant to our approach.
Semantic Web
Automated reasoning
Venkat Krishnamurthy
ac348f610320d103494dc36b09ca8f88a509e76c
YarcData
Krishnamurthy
Venkat
Venkat Krishnamurthy
Raghava Mutharaju
86122fca1f282f8027766d9b2d2ab1b56a078cd1
Kno.e.sis Center, Wright State University
Mutharaju
Raghava
Raghava Mutharaju
Chito Jovellanos
de997ccbe7342b078f3416db20446f0f64a47e00
forward look
Jovellanos
Chito
Chito Jovellanos
Yves Raimond
c99a220cc2b8dacc4d4de0342a3909c73eb4e2be
BBC
Raimond
Yves
Yves Raimond
Aristotle University of Thessaloniki
Aristotle University of Thessaloniki
events
EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content
reconciliation
semantic web
events
social services
social services
reconciliation
media
Houda Khrouf, Vuk Milicic and Raphaël Troncy
EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content
EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content
media
An ever increasing amount of event-centric knowledge is spread over multiple social services, either materialized as calendar of events or illustrated by media items shared by people. Crawling data and mining in real-time events connection between these heterogeneous services is a key challenge to enhance events views. In this paper, we present Event-Media, a web-based environment that exploits real-time connections to deliver rich content describing events associated with media, and interlined with the Linked Data cloud. EventMedia exploits semantic web technologies and provides user-friendly interface with the aim to meet the user needs: relive experiences based on media, and support decision making for attending upcoming events.
semantic web
events,media,semantic web,reconciliation,social services
Centre for Research and Technology Hellas (CERTH)
Centre for Research and Technology Hellas (CERTH)
Lantzaki
Christina Lantzaki
Christina
Christina Lantzaki
c18e69eed0eee4083b2373a5ed279dd42f56cb49
FORTH-ICS
Marcel Karnstedt
cb3d8caf8c55c72f11ec748f9a19c51a9fc8c91f
DERI, NUI Galway
Karnstedt
Marcel
Marcel Karnstedt
IBM Research Lab , Haifa, Israel
IBM Research Lab , Haifa, Israel
ISWC2012 Keynote Speaker
Alexey Boyarsky
7d67056b41145374f54fb83c800ac865f0afef23
EPFL
Boyarsky
Alexey
Alexey Boyarsky
Universidad Simon Bolivar
Universidad Simon Bolivar
2
Isabel Cruz
University of Illinois at Chicago
Cruz
Isabel
Isabel Cruz
Nikolaou
National and Kapodistrian University of Athens
Charalampos
d6aff7cc4a58c5d21d04ab22d4faa7bd2bdb00bf
Charalampos Nikolaou
Charalampos Nikolaou
Interoperability
Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case
Universal Instant Messaging
Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case
Verification
Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case
Interoperability,Composition,Ontology,Verification,Mediation,Universal Instant Messaging
Universal Instant Messaging
76500017
Mediation
Composition
Ontology
Amel Bennaceur, Valerie Issarny, Romina Spalazzese and Shashank Tyagi
Mediation
Composition
The success of pervasive computing depends on the ability to compose a multitude of networked applications dynamically in order to achieve user goals. However, applications from different providers are not able to interoperate due to incompatible interaction protocols or disparate data models. Instant messaging is a representative example of the current situation, where various competing applications keep emerging. To enforce interoperability at runtime and in a non-intrusive manner, mediators are used to perform the necessary translations and coordination between the heterogeneous applications. Nevertheless, the design of mediators requires considerable knowledge about each application as well as a substantial development effort. In this paper we present an approach based on ontology reasoning and model checking in order to generate correct-by-construction mediators automatically. We demonstrate the feasibility of our approach through a prototype tool and show that it synthesises mediators that achieve efficient interoperation of instant messaging applications.
Verification
Ontology
Interoperability
76500017
University of Oxford
Ian
Ian Horrocks
3361a8a2f71036d7ca03076a41f4d8ae08c71e97
Horrocks
Ian Horrocks
2012-11-15T14:00:00+05:00
2012-11-15T12:30:00+05:00
Lunch
Instance matching
2012-11-14T16:00:00+05:00
2012-11-14T17:30:00+05:00
60ab9a4dc6ed26b245c84595ffb60545d953e801
TU Darmstadt
Heiko Paulheim
Heiko Paulheim
Paulheim
Heiko
North Carolina State University
North Carolina State University
rule-based inferencing
SPARQL rules
RDF rules
76490273
complexity
The lightweight ontology language OWL RL is used for reasoning with large amounts of data. To this end, the W3C standard provides a simple system of deduction rules, which operate directly on the RDF syntax of OWL. Several similar systems have been studied. However, these approaches are usually complete for instance retrieval only. This paper asks if and how such methods could also be used for computing entailed subclass relationships. Checking entailment for arbitrary OWL RL class subsumptions is co-NP-hard, but tractable rule-based reasoning is possible when restricting to subsumptions between atomic classes. Surprisingly, however, this cannot be achieved in any RDF-based rule system, i.e., the W3C calculus cannot be extended to compute all atomic class subsumptions. We identify syntactic restrictions to mitigate this problem, and propose a rule system that is sound and complete for many OWL RL ontologies.
rule-based inferencing
complexity
The Not-So-Easy Task of Computing Class Subsumptions in OWL RL
RDF rules
The Not-So-Easy Task of Computing Class Subsumptions in OWL RL
Markus Krötzsch
SPARQL rules
spotlight
76490273
The Not-So-Easy Task of Computing Class Subsumptions in OWL RL
RDF rules,SPARQL rules,rule-based inferencing,complexity
ISWC2012 Panellist
To realize the Smart Cities vision, applications can leverage the large availability of open datasets related to urban environments. Those datasets need to be integrated, but it is often hard to automatically achieve a high-quality interlinkage. Human Computation approaches can be employed to solve such a task where machines are ineffective. We argue that in this case not only people's background knowledge is useful to solve the task, but also people's physical presence and direct experience can be successfully exploited. In this paper we present UrbanMatch, a Game with a Purpose for players in mobility aimed at validating links between points of interest and their photos; we discuss the design choices and we show the high throughput and accuracy achieved in the interlinking task.
Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch
76500033
Game with Purpose
Mobility
76500033
Mobility
Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch
Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch
Game with Purpose,Smart cities,Mobility
Smart cities
Irene Celino, Simone Contessa, Marta Corubolo, Daniele Dell'Aglio, Emanuele Della Valle, Stefano Fumeo and Thorsten Krueger
Game with Purpose
Smart cities
Bhavani Thuraisingham
b7461b7b01dfa88ddde27fac264fc1ee0ed9abdc
University of Texas at Dallas
Thuraisingham
Bhavani
Bhavani Thuraisingham
Pallavi Karanth
424c83c37480fe076d17371f2e4009687fdae253
PES Institute of Technology
Karanth
Pallavi
Pallavi Karanth
Milan Stankovic
Hypios
Milan
5bcf5873f64d7a8e7edb3d6ee7a19af043a82e81
9827f6c41cba244ffa9c1ac3f20a4cc6dd578e4a
Stankovic
Milan Stankovic
Jagannathan Srinivasan
Jagannathan
Jagannathan Srinivasan
Srinivasan
f3f12b445bccb179c3316d72820d4cd6e781cff0
ace4c6bb5c40723c227d7258cebc356a725e16a2
Oracle
ISWC2012 General Chair
Harth
AIFB, Karlsruhe Institute of Technology
Andreas Harth
Andreas
Andreas Harth
0604d144b935121a11d7495b2751e5d3176fc6fa
Ghent University
Ghent University
With SEKI@home, which stands for Search for Embedded Knowledge Items, we propose a generic, browser extension-based approach for crowdsourcing the task of knowledge extraction from arbitrary Web pages. As people with the extension installed browse a targeted Web page, the extension sends extracted knowledge items according to the customizable extraction rules to a centralized, optionally publicly accessible triple store. Thereby, simply by browsing the Web as usual, participants in the knowledge extraction task can help make previously locked-in knowledge openly accessible, e.g., via the standard SPARQL protocol. We have implemented and made available a prototype browser extension, which, after customization and adaptation, can serve as the basis for future knowledge extraction tasks.
knowledge extraction
browser extension
json-ld
SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages
semantic lifting
web scraping
semantic lifting
json-ld
SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages
knowledge extraction,browser extension,seki@home,web scraping,json-ld,semantic lifting
seki@home
knowledge extraction
SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages
Thomas Steiner and Stefan Mirea
browser extension
seki@home
web scraping
Riichiro Mizoguchi
36f7c5e541fbc21084f8bc4c85215922ab342097
Osaka University
Mizoguchi
Riichiro
Riichiro Mizoguchi
Elena Cabrio
346975d5d464d0d7523593d6bef915a11ff219c4
INRIA
Cabrio
Elena
Elena Cabrio
Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions
Semantic Stream
Applied Description Logics
Freddy Lecue, Anika Schumann and Marco Luca Sbodio
Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.
Semantic cities
Semantic integration
Semantic integration
Semantic cities
Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions
Semantic Stream
Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions
76500113
76500113
Applied Description Logics
Semantic Stream,Applied Description Logics,Semantic cities,Semantic integration
Pennsylvania State University
Pennsylvania State University
5ac8032d5f6012aa1775ea2f63e1676bafd5e80b
W3C
Herman
Ivan
Ivan Herman
Ivan Herman
Felix Sasaki
Felix
d261d1f6f8985467426d818be79cca445350921e
Felix Sasaki
DFKI
Sasaki
Semantic similarity-driven decision support in the skeletal dysplasia domain
Semantic similarity-driven decision support in the skeletal dysplasia domain
Ontologies
Semantic similarity
Semantic similarity
Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain.
76500161
Decision support
Ontologies
Semantic similarity-driven decision support in the skeletal dysplasia domain
Semantic similarity,Decision support,Ontologies
76500161
Decision support
Razan Paul, Tudor Groza, Andreas Zankl and Jane Hunter
2012-11-14T14:00:00+05:00
2012-11-14T12:30:00+05:00
Lunch
f1f7489808a1c44bbb8626c040a81260dcf55319
Nokia Services
Ora Lassila
Lassila
Ora Lassila
Ora
Ondrej Svab-Zamazal
fa5753d57260962506098825ba084e18a71f53f6
University of Economics, Prague
Svab-Zamazal
Ondrej
Ondrej Svab-Zamazal
Martin Serrano
ce606e6d2a74d6686ac48b6804bd59172b0f1930
DERI, NUI Galway
Serrano
Martin
Martin Serrano
ISWC2012 Session Chair
Evaluations and Experiments Track
2012-11-13T18:30:00+05:00
2012-11-13T17:30:00+05:00
Coffee Break
Paul Groth
Paul Groth
8f53bc4c411d120973ce1112da11af8ee6630854
VU Amsterdam
Groth
Paul
Trond Aalberg
7f46d911cde461f8d17efb913a74d69739d5189d
Norwegian University of Science and Technology
Aalberg
Trond
Trond Aalberg
Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools
manual semantic annotation
non-expert users
76490161
76490161
spotlight
usability
Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools
usability
Most of the semantic content available has been generated automatically by using annotation services for existing content. Automatic annotation is not of sufficient quality to enable focused search and retrieval: either too many or too few terms are semantically annotated. User-defined semantic enrichment allows for a more targeted approach. We developed a tool for semantic annotation of digital documents and conducted an end-user study to evaluate its acceptance by and usability for non-expert users. This paper presents the results of this user study and discusses the lessons learned about both the semantic enrichment process and our methodology of exposing non-experts to semantic enrichment.
Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools
non-expert users
Annika Hinze, Ralf Heese, Markus Luczak-Rösch and Adrian Paschke
manual semantic annotation
manual semantic annotation,non-expert users,usability
Technical University of Hamburg
Technical University of Hamburg
Brandeis University
Brandeis University
Michael Martin
University of Leipzig
Martin
Michael
Michael Martin
Tomas Vitvar
5b1941ad0dfc4b89c13abe7a20d0e1f71a7ff96a
Czech Technical University in Prague
Vitvar
Tomas
Tomas Vitvar
Jie Tang
Tsinghua University
Tang
Jie
Jie Tang
Pignotti
f38d4d85c208f258905be447b9779723dbb5a830
Edoardo Pignotti
Edoardo Pignotti
Edoardo
University of Aberdeen
2012-11-13T14:00:00+05:00
2012-11-13T12:30:00+05:00
Lunch
Epimorphics
Dave Reynolds
c496c063ea605640c8bd5ae7ab3adce9bbef360e
Dave
Dave Reynolds
Reynolds
Rolf Grütter
ebfcf3b9138cbb5c13a3511f20f055b3cecaa63f
Swiss Federal Institute for Forest, Snow and Landscape Research
Grütter
Rolf
Rolf Grütter
Royal Military College of Canada
Royal Military College of Canada
Houda Khrouf
e39c392263ad57cf60842b95a317046560efd4d4
EURECOM
Khrouf
Houda
Houda Khrouf
Domain Ontologies
Entity-quality structure
Knowledge representation
76500081
Experiences with modeling composite phenotypes in the SKELETOME project
Tudor Groza, Andreas Zankl and Jane Hunter
Domain Ontologies
Knowledge representation
76500081
Experiences with modeling composite phenotypes in the SKELETOME project
Entity-quality structure
Semantic annotation of patient data in the skeletal dysplasia domain (e.g., clinical summaries) is a challenging process due to the structural and lexical differences existing between the terms used to describe radiographic findings. In this paper we propose an ontology aimed at representing the intrinsic structure of such radiographic findings in a standard manner, in order to bridge the different lexical variations of the actual terms. Furthermore, we describe and evaluate an algorithm capable of mapping concepts of this ontology to exact or broader terms in the main phenotype ontology used in the bone dysplasia domain.
Domain Ontologies,Knowledge representation,Entity-quality structure
Experiences with modeling composite phenotypes in the SKELETOME project
Joseph Benik
af3fba8dce83f4e83cfea6e70336af0c387b466b
University of Maryland
Benik
Joseph
Joseph Benik
Rafael S. Gonçalves, Bijan Parsia and Ulrike Sattler
Semantic Diff
Concept-Based Semantic Difference in Expressive Description Logics
Concept-Based Semantic Difference in Expressive Description Logics
OWL Ontologies,Semantic Diff,NCI Thesaurus,Description Logics
Semantic Diff
Concept-Based Semantic Difference in Expressive Description Logics
NCI Thesaurus
76490097
OWL Ontologies
Description Logics
Detecting, much less understanding, the difference between two description logic based ontologies is challenging for ontology engineers due, in part, to the possibility of complex, non-local logic effects of axiom changes. First, it is often quite difficult to even determine which concepts have had their meaning altered by a change. Second, once a concept change is pinpointed, the problem of distinguishing whether the concept is directly or indirectly affected by a change has yet to be tackled. To address the first issue, various principled notions of ``semantic diff'' (based on deductive inseparability) have been proposed in the literature and shown to be computationally practical for the expressively restricted case of ELHr-terminologies. However, problems arise even for such limited logics as ALC: First, computation gets more difficult, becoming undecidable for logics such as SROIQ which underly the Web Ontology Language (OWL). Second, the presence of negation and disjunction make the standard semantic difference too sensitive to change: essentially, any logically effectual change always affects all terms in the ontology. In order to tackle these issues, we formulate the central notion of finding the minimal change set based on model inseparability, and present a method to differentiate changes which are specific to (thus directly affect) particular concept names. Subsequently we devise a series of computable approximations, and compare the variously approximated change sets over a series of versions of the NCI Thesaurus (NCIt).
76490097
Description Logics
NCI Thesaurus
OWL Ontologies
Anastasia Analyti
4a5cdf250f517ba7c69479854aa08bec803e7a7f
FORTH-ICS
Analyti
Anastasia
Anastasia Analyti
urban data
city data
city data
linked data
76500145
content platform
Vanessa Lopez, Spyros Kotoulas, Marco Luca Sbodio, Martin Stephenson, Aris Gkoulalas-Divanis and Pol Mac Aonghusa
urban data
76500145
privacy
QuerioCity: A Linked Data Platform for Urban Information Management
linked data
city data,content platform,linked data,urban data,privacy,stream data
In this paper, we present QuerioCity, a platform to catalog, index and query highly heterogenous information coming from complex systems, such as cities. A series of challenges are identified: namely, the heterogeneity of the domain and the lack of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), and the sensitivity of the information. We propose an approach for incremental and continuous integration of static and streaming data, based on Semantic Web technologies. The proposed system is unique in the literature in terms of handling of multiple integrations of available data sets in combination with flexible provenance tracking, privacy protection and continuous integration of streams. We report on lessons learnt from building the first prototype for Dublin.
stream data
content platform
privacy
QuerioCity: A Linked Data Platform for Urban Information Management
stream data
QuerioCity: A Linked Data Platform for Urban Information Management
ISWC2012 Session Chair
Li Tian
9b9f00d8b9230fb6fcbc9a6c2f4b01e5f8a7bfee
Shanghai Jiao Tong University
Tian
Li
Li Tian
University of Sheffield
d7ee6f0673af6e031b1096ee5870da5e3fea4050
Elbedweihy
Khadija
Khadija Elbedweihy
Khadija Elbedweihy
Workshop Chair
Yulan He
Yulan He
KMi, The Open University
a25aba451a126e8c0524bb7aa32862cae59b6470
Yulan
He
b86f92857f5251e30a79eb958165f51871c72ac8
Jacco Van Ossenbruggen
55937258805a240393727f8e803f0c787135bb91
Centrum Wiskunde & Informatica (CWI)
Van Ossenbruggen
Jacco
Jacco Van Ossenbruggen
ISWC2012 Session Chair
Graz University of Technology
Graz University of Technology
Denilson Barbosa
7a5509a7eb7ac9f4ddc981bbee622fe02de12438
University of Alberta
Barbosa
Denilson
Denilson Barbosa
Shenghui Wang
Shenghui Wang
Shenghui
229b7f6634fe989f466d6029739c24aca58dfba3
Wang
OCLC
Prasenjit Mitra
Pennsylvania State University
Mitra
Prasenjit
Prasenjit Mitra
Rommel N.
Rommel N. Carvalho
2baa083d5522da52321c52d9a57a93ae837bb277
Rommel N. Carvalho
Carvalho
George Mason University
1
Norman Heino
f573c7cf859e0924ed074c08d0099f9bdd08211b
University of Leipzig
Heino
Norman
Norman Heino
Benoit Christophe
Alcatel-Lucent Bell Labs France
Christophe
Benoit
Benoit Christophe
Taxonomy of items
Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy
Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy
Collaborative filtering
Recommendation over temporal dynamics
Recommendation over temporal dynamics
Collaborative filtering
Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy
Makoto Nakatsuji, Yasuhiro Fujiwara, Toshio Uchiyama and Hiroyuki Toda
Time weight collaborative filtering
76490353
Taxonomy of items
Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users' transactions into epochs i.e. time periods, and identify epochs that are dominated by interests similar to the current interests of the active user. Thus, it can eliminate dissimilar transactions while making use of similar transactions that exist in prior epochs. Second, we use a taxonomy of items to model user item transactions in each epoch. This well captures the interests of users in each epoch even if there are few transactions. It suits the situations in which the items transacted by users dynamically change over time; the semantics behind classes do not change so often while individual items often appear and disappear. Fortunately, many taxonomies are now available on the web because of the spread of the Linked Open Data vision. We can now use those to understand dynamic user interests semantically. We evaluate our method using a dataset, a music listening history, extracted from users' tweets and one containing a restaurant visit history gathered from a gourmet guide site. The results show that our method predicts user interests much more accurately than the previous time-decay-based method.
76490353
Collaborative filtering,Taxonomy of items,Recommendation over temporal dynamics,Time weight collaborative filtering
Time weight collaborative filtering
26a374ad4de937252c044f7dd45aa48ecbdb4f16
Steve Harris
Garlik
Steve Harris
Harris
Steve
Purdue University
Purdue University
2012-11-14T17:45:00+05:00
Town Hall Meeting
2012-11-14T18:45:00+05:00
Introduced at ISWC 2009 it has become a tradition at ISWC to come together in a town hall meeting to have conference participants share ideas on what they would like to see at the future ISWCs and to discuss what works and what does not work at the conference. Please join the members of the conference organizing committee in an informal discussion about all the new events that we added to the conference program this year and tell us what you would like to see in the future and what you liked and didn't like this year.
Czech Technical University in Prague
Czech Technical University in Prague
Yutaka Matsuo
University of Tokyo
Matsuo
Yutaka
Yutaka Matsuo
2012-11-13T16:00:00+05:00
2012-11-13T17:30:00+05:00
Knowledge Discovery
1
Raymond Lloyd
cf196394170671ee44ac163dea7c63f307a6e9d4
IBM Research
Lloyd
Raymond
Raymond Lloyd
MIT
MIT
Deborah L. McGuinness
292f7f25a21bd6c41c784397092317016dfc987d
McGuinness
RPI
8e2d61e147457c49ea020e33df12d9eb67ff1bbe
Deborah L. McGuinness
Deborah
Harald Sack
4d582458728456ce41559e03c6b992bf68a8268d
Hasso Plattner Institute
Sack
Harald
Harald Sack
Robert Engels
Engels
608c13d67ab3e5fe3be33c7b9f443ffeba4d3883
Robert Engels
Robert
self
Ontology Dynamics
Visualization and Presenation of Evolving Knowledge
Temporality in Knowledge Capturing
EvoDyn 2012 continues the tradition of EvoDyn 2011 and the IWOD workshop series in being the core annual event to discuss advances in the broad area of ontology dynamics, and to track recent work directly or indirectly related to the problem of evolving knowledge. The workshop focuses on analysis of trends and change in formal descriptions, but also in associated raw sources of knowledge (scientific publications, unstructured or semi-structured Web content, traditional data stores, e-mail or on-line discussion threads, etc.). We are especially interested in research targeted on various states of knowledge evolution, such as (a) conflicts, (b) consolidation, (c) discovery, (d) paradigm shifts, and (e) breakthroughs. We would like to trigger a comprehensive and coherent approach to studying the process of knowledge evolution by bringing together researchers and practitioners from the following fields: (i) Data mining and knowledge discovery in dynamic resources; (ii) Ontology dynamics and versioning; (iii) Trend analysis (in multiple applications, including internet search, corpus evaluation, etc.); (iv) Natural Language Processing (evolution of terminology, language use, semantics); (v) Knowledge Representation (temporal ontologies, temporal logics, belief revision, etc.); (vi) Discourse Analysis and Philosophy of Science (the definition and understanding of what particular phases of the knowledge evolution are, and how can we delimit, identify or even trigger them).
Knowledge Evolution
Ontology Dynamics
2012-11-12T09:00:00+05:00
EvoDyn
Knowledge Integration and Analysis over Time
Knowledge Evolution
2012-11-12T17:30:00+05:00
2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics
Knowledge Integration and Analysis over Time
Representation of and Reasoning on Evolving Knowledge
Temporality in Knowledge Capturing
Ontology Dynamics, Knowledge Evolution, Temporality in Knowledge Capturing, Knowledge Integration and Analysis over Time, Visualization and Presenation of Evolving Knowledge, Representation of and Reasoning on Evolving Knowledge
Representation of and Reasoning on Evolving Knowledge
Visualization and Presenation of Evolving Knowledge
3
2
Ontologies
Linked Data
BioPortal is a repository of biomedical ontologies - the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other languages, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF based serializations of all these ontologies and their metadata at sparql.bioontology.org. This dataset contains 203M triples, representing both content and metadata for the 300+ ontologies; and 9M mappings between terms. This endpoint can be queried with SPARQL which opens new usage scenarios for the biomedical domain. This paper presents lessons learned from having redesigned several applications that today use this SPARQL endpoint to consume ontological data.
SPARQL
Linked Data
Manuel Salvadores, Matthew Horridge, Paul Alexander, Ray W. Fergerson, Mark A. Musen and Natasha F. Noy
Ontologies,SPARQL,RDF,Biomedical,Linked Data
76500177
Using SPARQL to Query BioPortal Ontologies and Metadata
RDF
76500177
RDF
Ontologies
Biomedical
Using SPARQL to Query BioPortal Ontologies and Metadata
Using SPARQL to Query BioPortal Ontologies and Metadata
Biomedical
SPARQL
classificational interpretation of mappings
76490017
A Formal Semantics for Weighted Ontology Mappings
76490017
weighted mappings,degree of satisfiability of a mapping,classificational interpretation of mappings
spotlight
weighted mappings
degree of satisfiability of a mapping
Ontology mappings are often assigned a weight or confidence factor by matchers. Nonetheless, few semantic accounts have been given so far for such weights. This paper presents a formal semantics for weighted mappings between different ontologies. It is based on a classificational interpretation of mappings: if O1 and O2 are two ontologies used to classify a common set X , then mappings between O1 and O2 are interpreted to encode how elements of X classified in the concepts of O1 are re-classified in the concepts of O2, and weights are interpreted to measure how precise and complete re-classifications are. This semantics is justifiable by extensional practice of ontology matching. It is a conservative extension of a semantics of crisp mappings. The paper also includes properties that relate mapping entailment with description logic constructors.
A Formal Semantics for Weighted Ontology Mappings
Manuel Atencia, Alexander Borgida, Jérôme Euzenat, Chiara Ghidini and Luciano Serafini
degree of satisfiability of a mapping
classificational interpretation of mappings
A Formal Semantics for Weighted Ontology Mappings
weighted mappings
Yong-Bin Kang
03592bb632c380a23e5b27855f7488fd2bd219d6
Monash University
Kang
Yong-Bin
Yong-Bin Kang
2012-11-13T17:30:00+05:00
2012-11-13T17:00:00+05:00
schema.org update
Pascal Hitzler
d9d5e01de07e6f9a5e8b66c44c995c5ca8cc3b63
Pascal Hitzler
Hitzler
Pascal
Kno.e.sis Center, Wright State University
47df72dfef38eb3cd02cb6259bb46bcb5f5c55f3
Whitier
Posters and Demos
spotlight
Milan Dojchinovski, Jaroslav Kuchar, Tomas Vitvar and Maciej Zaremba
Web APIs
social network
Web APIs
service selection
Personalised Graph-based Selection of Web APIs
Web services
social network
Personalised Graph-based Selection of Web APIs
Web services
service selection
Personalised Graph-based Selection of Web APIs
76490033
76490033
personalisation
ranking
Web APIs, Web services, personalisation, ranking, service selection, social network
personalisation
Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this paper, we present a semantic model of a Web API directory graph that captures relationships such as Web APIs, mashups, developers, and categories. We describe a novel configurable graph-based method for selection of Web APIs with personalised and temporal aspects. The method allows users to get more control over their preferences and recommended Web APIs while they can exploit information about their social links and preferences. We evaluate the method on a real-world dataset from ProgrammableWeb.com, and show that it provides more contextualised results than currently available popularity based rankings.
ranking
Arantza Illarramendi
Basque Country University
Illarramendi
Arantza
Arantza Illarramendi
Antidot
Antidot
Peter Boncz
Boncz
Peter
5f5b73584c42f1fc4d222cb3633ac432b101a16a
Peter Boncz
da89467b6c6397e5e8ebac0f8c307fabc54664b4
Centrum Wiskunde & Informatica (CWI)
Alberto Lavelli
05ca6f4ae400a2e99af6d881295feb059c9f6645
Fondazione Bruno Kessler
Lavelli
Alberto
Alberto Lavelli
Lehigh University
Lehigh University
Thomas Gottron
9c5c70601201113cdb69fc9026b89b5ea04067f0
University of Koblenz and Landau
Gottron
Thomas
Thomas Gottron
Industry Track IV
2012-11-14T17:30:00+05:00
2012-11-14T16:00:00+05:00
Bijan Parsia
Bijan Parsia
Bijan
c9ef3edda6fd12cc1e9113217a75e5074e2ee80e
Parsia
University of Manchester
Linköping University
Eva Blomqvist
6ae81c4db26362dadb297719b4178e23dd011c6d
Eva Blomqvist
Eva
f346d71f8a4b81034daeeb94d93d4eb7c0c8157f
Blomqvist
Carsten Keßler
University of Münster
Keßler
Carsten
Carsten Keßler
Charles University in Prague
Charles University in Prague
Ontology Matching
Discovering Concept Coverings in Ontologies of Linked Data Sources
Discovering Concept Coverings in Ontologies of Linked Data Sources
Ontology Matching
Linked Data
spotlight
76490417
Rahul Parundekar, Craig Knoblock and José Luis Ambite
Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability goal of the Semantic Web. In this paper, we address this problem by finding alignments between concepts from multiple Linked Data sources. Instead of only considering the existing concepts present in each ontology, we hypothesize new composite concepts defined as disjunctions of conjunctions of (RDF) types and value restrictions, which we call restriction classes, and generate alignments between these composite concepts. This extended concept language enables us to find more complete definitions and to even align sources that have rudimentary ontologies, such as those that are simple renderings of relational databases. Our concept alignment approach is based on analyzing the extensions of these concepts and their linked instances. Having explored the alignment of conjunctive concepts in our previous work, in this paper, we focus on concept coverings (disjunctions of restriction classes). We present an evaluation of this new algorithm to Geospatial, Biological Classification, and Genetics domains. The resulting alignments are useful for refining existing ontologies and determining the alignments between concepts in the ontologies, thus increasing the interoperability in the Linked Open Data Cloud.
Discovering Concept Coverings in Ontologies of Linked Data Sources
Schema Alignment
Linked Data
Schema Alignment
Ontology Matching,Schema Alignment,Linked Data
76490417
2012-11-13T15:30:00+05:00
Queries
2012-11-13T14:00:00+05:00
Lockheed Martin
Lockheed Martin
Kouji Kozaki, Hiroko Kou, Yuki Yamagata, Takeshi Imai, Kazuhiko Ohe and Riichiro Mizoguchi
Browsing Causal Chains in a Disease Ontology
disease ontology
ontology visualization
causal chain
Browsing Causal Chains in a Disease Ontology
In order to realize sophisticated medical information systems, many medical ontologies have been developed. We proposed a definition of disease based on River Flow Model which captures a disease as a causal chain of clinical disorders. We also developed a disease ontology based on the model. It includes definitions of more than 6,000 diseases with 17,000 causal relationships. This demonstration summarizes the disease ontology and a browsing system for causal chains defined in it.
Browsing Causal Chains in a Disease Ontology
disease ontology
causal chain
disease ontology,causal chain,ontology visualization
ontology visualization
Umbrich
Jürgen Umbrich
b9514eba2ed2ae41a072765fe3ed3544de10974f
DERI, NUI Galway
Jürgen
Jürgen Umbrich
SAP Labs USA
SAP Labs USA
Policies
In this paper we discuss our experience with the design, development and deployment of the ourSpaces Virtual Research Environment. ourSpaces makes use of Semantic Web technologies to create a platform to support multidisciplinary research groups. This paper introduces the main semantic components of the system: a framework to capture the provenance of the research process, a collection of services to create and visualise metadata and a policy reasoning service. We also describe different approaches to support interaction between users and metadata within the VRE. We discuss the lessons learnt during the deployment process with three case study groups. Finally, we present our conclusions and future directions for exploration in terms of developing ourSpaces further.
Provenance,Virtual Research Environment,Policies,NLG
Provenance
ourSpaces - Design and Deployment of a Semantic Virtual Research Environment
ourSpaces - Design and Deployment of a Semantic Virtual Research Environment
NLG
NLG
Virtual Research Environment
Peter Edwards, Edoardo Pignotti, Alan Eckhardt, Kapila Ponnamperuma, Chris Mellish and Thomas Bouttaz
76500049
Provenance
ourSpaces - Design and Deployment of a Semantic Virtual Research Environment
76500049
Policies
Virtual Research Environment
Daniel
Daniel Gerber
7716ea9608422625fa7ba043975f357ef856060f
Daniel Gerber
University of Leipzig
Gerber
Sapienza Università di Roma
Sapienza Università di Roma
1
Aristotle University of Thessaloniki
930225eb9728dd814a0580a32d4104850ae6d0c5
Efstratios Kontopoulos
Efstratios Kontopoulos
Kontopoulos
Efstratios
Oleg Ruchayskiy
ed43ece6fa6d690c8c0930d1bb787fe744c12cd1
CERN
Ruchayskiy
Oleg
Oleg Ruchayskiy
2012-11-11T09:00:00+05:00
PSW
Information and Data Management
The Semantic Web is growing at an enormous pace. However, the development of semantic web software applications is not yet mainstream. Reasons for that include one (or more) of the following research issues: lack of integrated development environments (IDEs, such as Visual Studio and Eclipse), poor programming language support, lack of standard testbeds and/or benchmarks, inadequate training, and perhaps the need for curricula revision. Properly addressing these issues requires interdisciplinary skills, and the collaboration between academia and industry. The First Workshop on Programming the Semantic Web invites submissions that explore the gap between todays semantic web challenges, particularly the ones related to dealing with large amounts of data, with the lack of adequate tools. We are looking for contributions that discuss, promote and further advance the programming facet of the semantic web, including the development of new languages, extension of existing ones, and the inclusion of semantic enabled capabilities into existing IDEs.
Programming languages for the Semantic Web, Information and Data Management, Semantic Web Technology Stack
Information and Data Management
2012-11-11T17:30:00+05:00
First Workshop on Programming the Semantic Web
Semantic Web Technology Stack
Programming languages for the Semantic Web
Programming languages for the Semantic Web
Semantic Web Technology Stack
Bernie Innocenti
d5ef8a624230622cd0cae57440693e6a9fcba519
Sugar Labs
Innocenti
Bernie
Bernie Innocenti
Mikko Rinne
Rinne
bcdbe37390fab64e570d73c4efbc4b9322097c9f
Aalto University
Mikko Rinne
Mikko
University of Hannover
University of Hannover
1
Lehmann
Jens
Jens Lehmann
01fee219e665ecea3905f361517b2bd4a344975d
University of Leipzig
Jens Lehmann
cdecab02f3fb1d3d9c79e1a8a8730bd11ef9f2d3
Timothy Lebo
Lebo
Timothy Lebo
RPI
Timothy
Technion
Technion
2012-11-14T11:00:00+05:00
Provenance and Verification
2012-11-14T12:30:00+05:00
2012-11-14T16:00:00+05:00
Industry Track III
2012-11-14T17:30:00+05:00
PC Member at ISWC2012(Research Track)
Semantic Web Company GmbH
Semantic Web Company GmbH
Managing the life-cycle of Linked Data with the LOD2 Stack
Linked Data
The LOD2 Stack is an integrated distribution of aligned tools which support the whole life cycle of Linked Data from extraction, authoring/creation via enrichment, interlinking, fusing to maintenance. The LOD2 Stack comprises new and substantially extended existing tools from the LOD2 project partners and third parties. The stack is designed to be versatile; for all functionality we define clear interfaces, which enable the plugging in of alternative third-party implementations. The architecture of the LOD2 Stack is based on three pillars: (1) Software integration and deployment using the Debian packaging system. (2) Use of a central SPARQL endpoint and standardized vocabularies for knowledge base access and integration between the different tools of the LOD2 Stack. (3) Integration of the LOD2 Stack user interfaces based on REST enabled Web Applications. These three pillars comprise the methodological and technological framework for integrating the very heterogeneous LOD2 Stack components into a consistent framework. In this article we describe these pillars in more detail and give an overview of the individual LOD2 Stack components. The article also includes a description of a real-world usage scenario in the publishing domain.
Linked Data
Linked Data,application integration,Provenance
Managing the life-cycle of Linked Data with the LOD2 Stack
application integration
Sören Auer, Lorenz Bühmann, Christian Dirschl, Orri Erling, Michael Hausenblas, Robert Isele, Jens Lehmann, Michael Martin, Pablo N. Mendes, Bert van Nuffelen, Claus Stadler, Sebastian Tramp and Hugh Williams
76500001
Managing the life-cycle of Linked Data with the LOD2 Stack
application integration
76500001
Provenance
Provenance
KAIST
KAIST
Iowa State University
Iowa State University
Harokopio University of Athens
Harokopio University of Athens
Oak Ridge National Laboratory
Oak Ridge National Laboratory
Fouad Zablith
5f4ccbf95c8b46eca5bd1ef1dc1d6b39970d0554
KMi, The Open University
Zablith
Fouad
Fouad Zablith
NICT
NICT
Roger Hall
UALR
Hall
Roger
Roger Hall
Charles
National Library of Medicine
bf554f67d7b26477e3bf5bf955d4caacdc54a2d0
Olivier Bodenreider
Olivier Bodenreider
Olivier
Bodenreider
Alasdair J. G. Gray
University of Manchester
Gray
Alasdair
Alasdair J. G. Gray
Vanessa Lopez
5c3ac25297fd6033d663d292004b1e8d977ceb5f
IBM Research
Lopez
Vanessa Lopez
Vanessa
Jamie Taylor
dd485fcb6436ae2a96fe33a418ee96e3f5293be1
Metaweb
Taylor
Jamie
Jamie Taylor
Gianluca
Demartini
8ec78d4c2f61e72bfc53da2988a051c97c9c2c7f
Gianluca Demartini
Gianluca Demartini
University of Fribourg
2012-11-14T11:00:00+05:00
Industry Track II
2012-11-14T12:30:00+05:00
2012-11-14T11:00:00+05:00
2012-11-14T12:30:00+05:00
Alternative Knowledge Representation Approaches
Holger Wache
612ab1009b9f9bb7c31f53982a6de846e572dfff
University of Applied Sciences and Arts Northwestern Switzerland
Wache
Holger
Holger Wache
Qunzhi Zhou
7e8834512c5d648768dbdca54a012b245e85634b
University of Southern California
Zhou
Qunzhi
Qunzhi Zhou
ISWC2012
11th International Semantic Web Conference
Tania Tudorache
Tudorache
Stanford University
Tania Tudorache
638d023ba44fb531a81881698239ebb410af4790
Tania
PC Member at ISWC2012(Doctoral Consortium)
University of Wuerzburg
University of Wuerzburg
Klaas Dellschaft
eae2717871287ee074d3e89a2ea58c2ee0026b8b
University of Koblenz-Landau
Dellschaft
Klaas
Klaas Dellschaft
Nuance Communications
Nuance Communications
Oktie Hassanzadeh
1d9481a1e0d0c956587cf8457e6fd8fda97effdb
IBM Research
Hassanzadeh
Oktie
Oktie Hassanzadeh
2
NER
Creating Enriched YouTube Media Fragments With NERD Using Timed-Text
Media fragment
Yunjia Li, Giuseppe Rizzo and Raphaël Troncy
Creating Enriched YouTube Media Fragments With NERD Using Timed-Text
NER
Media annotation
This demo enables the automatic creation of semantically annotated YouTube media fragments. A video is first ingested in the Synote system and a new method enables to retrieve its associated subtitles or closed captions. Next, NERD is used to extract named entities from the transcripts which are then temporally aligned with the video. The entities are disambiguated in the LOD clound and a user interface enables to browse through the entities detected in a video or get more information. We evaluated our application with 60 videos from 3 YouTube channels.
Creating Enriched YouTube Media Fragments With NERD Using Timed-Text
Media fragment,Media annotation,NER
Media annotation
Media fragment
Ying Zhang
398b8d7fa1a9c358bf21f3310c7255fd6a791129
Centrum Wiskunde & Informatica (CWI)
Zhang
Ying
Ying Zhang
James Michaelis
890d7402bc41cdab3ce5ce860a6b46bf9903d32c
RPI
Michaelis
James
James Michaelis
2012-11-14T17:30:00+05:00
2012-11-14T16:00:00+05:00
This session provides an open forum for participants to present a topic of their choosing. Each presenter is limited to one slide and two minutes time. Presenters must submit the title and one pdf slide to an email address announced in the conference's opening session. Limited presentation slots will be awarded on a first-come-first-serve basis.
Lightning Talks
University of Liverpool
University of Liverpool
ISWC2012 Session Chair
University of Manchester
University of Manchester
Samsung Information Systems America
Samsung Information Systems America
SKOS
A key objective of multidimensional dataset analysis is to reveal patterns of interest to analysts. However, multidimensional analysis has been observed to be dicult for analysts, due to the challenges of both presenting and navigating large datasets. This work explores how initial summarizations of multidimensional datasets can be generated for consuming parties (designed to reduce the number of data points which would need to be displayed) driven by summarization policies based on provided dataset values. Additionally, functionality for explaining the derivation of summarizations is being developed - in line with prior work on aiding analyst interactions with data processing systems. To help drive development of this work, as well as provide illustrative use cases, we are presently developing a dataset summarization generator as part of greater work being done in the Foresight and Understanding from Scientific Exposition (FUSE) program.
Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization
OLAP,SKOS,Provenance
OLAP
Provenance
James Michaelis, Deborah L. McGuinness, Cynthia Chang, Joanne Luciano and Jim Hendler
SKOS
Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization
OLAP
Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization
Provenance
Linked Data
2012-11-13T11:00:00+05:00
2012-11-13T12:30:00+05:00
Birmingham City University
Birmingham City University
2012-11-13T09:00:00+05:00
2012-11-13T08:30:00+05:00
Opening Ceremony
Strabon: A Semantic Geospatial DBMS
SPARQL
geospatial information
Strabon: A Semantic Geospatial DBMS
Kostis Kyzirakos, Manos Karpathiotakis and Manolis Koubarakis
SPARQL
We present Strabon, a new RDF store that supports the state of the art semantic geospatial query languages stSPARQL and GeoSPARQL. To illustrate the expressive power offered by these query languages and their implementation in Strabon, we concentrate on the new version of the data model stRDF and the query language stSPARQL that we have developed ourselves. Like GeoSPARQL, these new versions use OGC standards to represent geometries where the original versions used linear constraints. We study the performance of Strabon experimentally and show that it scales to very large data volumes and performs, most of the times, better than all other geospatial RDF stores it has been compared with.
geospatial information
RDF
RDF, SPARQL, geospatial information
Strabon: A Semantic Geospatial DBMS
76490289
76490289
RDF
RDF/OWL
Semantic Web
Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product
Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product
Semantic Web,RDF/OWL,Linked Data,Enterprise,Commercial
Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product
Chris Chaulk
Enterprise
Commercial
RDF/OWL
Enterprise
Commercial
Linked Data
Linked Data
Semantic Web
c5eb2a2d51e4a1308410f623bafd4f86c3120def
Ioannis Vlahavas
Ioannis Vlahavas
Aristotle University of Thessaloniki
Vlahavas
Ioannis
EMC R&D Brazil
EMC R&D Brazil
Web of Data
Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró
Semantic Navigation
Script Language
Script Language
Semantic Navigation on the Web with swget
Web of Data
Semantic Navigation
Semantic navigation in the Web of Data is crucial to exploit the power of thousand of RDF data sources today available. We present swget, a tool that enables to perform selective navigation of distributed semantic data sources, triggering of actions over data encountered during the navigation, retrieval of data and extraction of relevant Web fragments. At the core of swget there is a powerful navigational language called NautiLOD with a concise syntax and a formal semantics. swget can be exploited to write declarative specifications of information on the Web in the form of scripts that can be shared, mixed, and reused. We describe the architecture of the swget tool and present both a standalone version and an online portal where users can create their intelligent agents, launch them and be notified when results are ready. Besides, swget also provide an appealing visualization tool to explore and make sense of results
Semantic Navigation on the Web with swget
Semantic Navigation on the Web with swget
Semantic Navigation,Script Language,Web of Data
Alcatel-Lucent Bell Labs France
Alcatel-Lucent Bell Labs France
Hu
f7074d05b74deb43ec150671bb9b226578d20f2b
Nanjing University
Wei
Wei Hu
Wei Hu
Lyndon Nixon
fedfee89e4699ebbd40cfd1aa1c44a1fd0220d7d
STI International
Nixon
Lyndon
Lyndon Nixon
1
Norwegian University of Science and Technology
Norwegian University of Science and Technology
Oracle
Zhe Wu
Wu
Zhe Wu
Zhe
f7c491cd2bf21a4fb49b3bcf3d1de571e97b34c7
3
ISWC2012 Poster and Demo Chair
Testbeds proposed so far to evaluate, compare, and eventually improve SPARQL query federation systems have still some limitations. Some variables and configurations that may have an impact on the behavior of these systems (e.g., network latency, data partitioning and query properties) are not sufficiently defined; this affects the results and repeatability of independent evaluation studies, and hence the insights that can be obtained from them. In this paper we evaluate FedBench, the most comprehensive testbed up to now, and empirically probe the need of considering additional dimensions and variables. The evaluation has been conducted on three SPARQL query federation systems, and the analysis of these results has allowed to uncover properties of these systems that would normally be hidden with the original testbeds.
Federated Semantic Data Query Processing Strategies
SPARQL Endpoints
Gabriela Montoya, Maria-Esther Vidal, Oscar Corcho, Edna Ruckhaus and Carlos Buil-Aranda
Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?
76500306
Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?
Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?
Benchmarking
Federated Semantic Data Query Processing Strategies
Benchmarking,Federated Semantic Data Query Processing Strategies,SPARQL Endpoints
SPARQL Endpoints
76500306
Benchmarking
Laurens Rietveld
43940ba2ec7ad17ad9221094b5983ceb934b798e
VU Amsterdam
Rietveld
Laurens
Laurens Rietveld
Adrian Paschke
c352377f5d48b16e5e29253acaee884ebce39d74
Free University of Berlin
Paschke
Adrian
Adrian Paschke
Konrad Höffner
4ffd792932126fe6ab4d187c84c65c0f9c665057
University of Leipzig
Höffner
Konrad
Konrad Höffner
ISWC2012 Session Chair
John Breslin
DERI, NUI Galway
Breslin
John
John Breslin
comparing RDF/S knowledge bases,mapping blank nodes,versioning and synchronization
mapping blank nodes
versioning and synchronization
comparing RDF/S knowledge bases
comparing RDF/S knowledge bases
Blank Node Matching and RDF/S Comparison Functions
Yannis Tzitzikas, Christina Lantzaki and Dimitris Zeginis
mapping blank nodes
versioning and synchronization
76490577
Blank Node Matching and RDF/S Comparison Functions
In RDF, a blank node (or anonymous resource or bnode) is a node in an RDF graph which is not identified by a URI and is not a literal. Several RDF/S Knowledge Bases (KBs) rely heavily on blank nodes as they are convenient for representing complex attributes or resources whose identity is unknown but their attributes (either literals or associations with other resources) are known. In this paper we show how we can exploit blank nodes anonymity in order to reduce the delta (diff) size when comparing such KBs. The main idea of the proposed method is to build a mapping between the bnodes of the compared KBs for reducing the delta size. We prove that finding the optimal mapping is NP-Hard in the general case, and polynomial in case there are not directly connected bnodes. Subsequently we present various polynomial algorithms returning approximate solutions for the general case. For making the application of our method feasible also to large KBs we present a signature-based mapping algorithm with n logn complexity. Finally, we report experimental results over real and synthetic datasets that demonstrate significant reductions in the sizes of the computed deltas. For the proposed algorithms we also provide comparative results regarding delta reduction, equivalence detection and time efficiency.
Blank Node Matching and RDF/S Comparison Functions
76490577
Siegfried
DERI, NUI Galway
583cac1297018405882f186064c6d98cd127af70
Siegfried Handschuh
Siegfried Handschuh
Handschuh
Lexington
Universidad de Zaragoza
Fernando Bobillo
Bobillo
Fernando Bobillo
Fernando
9a2a51c421e2f974324e2006f134f0353ce9ad61
Stardog Linked Data Catalog
Linked Data
Linked Data,Data Catalog,Data integration
Data Catalog
Linked Data
Stardog Linked Data Catalog
Data integration
Data integration
Evren Sirin and Kendall Clark
Data Catalog
Stardog Linked Data Catalog
Luciano Serafini
7fea00a39da1a3986831556109303fc904b9f935
Luciano
Luciano Serafini
Fondazione Bruno Kessler
Serafini
University of Milano-Bicocca
University of Milano-Bicocca
Domain Entity Annotation
Domain Entity Annotation,Linked Open Data,Collective Annotation
Collective Annotation
Recently, with the ever-growing of textual medicine records, annotating domain entities has been regarded as an important task in the biomedical field. On the other hand, the process of interlinking open data sources is actively pursued within the Linking Open Data (LOD) project. The number of entities and the number of properties describing semantic relationships between entities within the linked data cloud are very large. In this paper, we propose a knowledge-incentive approach based on LOD for entity annotation in the biomedical field. With this approach, we implement MeDetect, a prototype system to solve the problems mentioned above. The experimental results verify the effectiveness and efficiency of our approach.
MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data
Li Tian, Weinan Zhang, Haofen Wang, Chenyang Wu, Yuan Ni, Feng Cao and Yong Yu
MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data
Domain Entity Annotation
Linked Open Data
MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data
Linked Open Data
Collective Annotation
Josh Hanna
25ac417526d190329bcaa2a1ad47c6fb405eca5d
UAMS
Hanna
Josh
Josh Hanna
Nicola Guarino
ISTC-CNR
Guarino
Nicola
Nicola Guarino
Nathan Wilson
6173d79946f322357698f37a35613df9b0ea5412
Marine Biological Laboratory
Wilson
Nathan
Nathan Wilson
Siemens AG
Siemens AG
Purdue University
Jan Vitek
2e4c9e6c607e2995e126a3831a649d982e38b8f3
Jan Vitek
Vitek
Jan
A graduate of University of Oxford, Tim Berners-Lee invented the World Wide Web, an internet-based hypermedia initiative for global information sharing while at CERN, the European Particle Physics Laboratory, in 1989. He wrote the first web client and server in 1990. His specifications of URIs, HTTP and HTML were refined as Web technology spread. He is the 3Com Founders Professor of Engineering in the School of Engineering with a joint appointment in the Department of Electrical Engineering and Computer Science at the Laboratory for Computer Science and Artificial Intelligence (CSAIL) at the Massachusetts Institute of Technology (MIT) where he also heads the Decentralized Information Group (DIG). He is also a Professor in the Electronics and Computer Science Department at the University of Southampton, UK. He is the Director of the World Wide Web Consortium (W3C), a Web standards organization founded in 1994 which develops interoperable technologies (specifications, guidelines, software, and tools) to lead the Web to its full potential. He was a Director of the Web Science Trust (WST) launched in 2009 to promote research and education in Web Science, the multidisciplinary study of humanity connected by technology. Tim is a Director of the World Wide Web Foundation, launched in 2009 to coordinate efforts to further the potential of the Web to benefit humanity.
W3C
Tim
Tim Berners-Lee
Berners-Lee
Tim Berners-Lee
Myunggwon Hwang
24d25e5f8456e57401071561b732f03219d69ff6
Korea Institute of Science and Technology Information
Hwang
Myunggwon
Myunggwon Hwang
83f86224b1408a5b9824b769231ecc20244cc1ff
Karl Aberer
Karl Aberer
EPFL
Karl
Aberer
2
PC Member at ISWC2012(Poster and Demo)
Amel Bennaceur
028205a96123a1988acee594ff6124e01f7ef03b
INRIA
Bennaceur
Amel
Amel Bennaceur
Cardiff University
Cardiff University
Alex Crow
af030da6d69716804cc2943a3be21f36596501b5
UALR
Crow
Alex
Alex Crow
ISWC2012 Session Chair
PC Member at ISWC2012
Workshop Chair
sensor data
streaming data
SRBench: A Streaming RDF/SPARQL Benchmark
benchmark
SRBench: A Streaming RDF/SPARQL Benchmark
We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet comprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.
streaming data
sensor data
benchmark,streaming RDF/SPARQL,streaming data,sensor data
76490625
streaming RDF/SPARQL
Ying Zhang, Minh-Duc Pham, Oscar Corcho and Jean Paul Calbimonte
streaming RDF/SPARQL
benchmark
76490625
SRBench: A Streaming RDF/SPARQL Benchmark
sparql
earth observation
linked geospatial data
rdf
Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies
fire monitoring
Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies
TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. In this demo paper we demonstrate a fire monitoring service that we have implemented in context of the project TELEIOS and explain how Semantic Web and Linked Data technologies allow the service to go beyond relevant services currently deployed in various Earth Observation data centers.
fire monitoring
Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies
semantic web
linked geospatial data
Kostis Kyzirakos, Manos Karpathiotakis, George Garbis, Charalampos Nikolaou, Konstantina Bereta, Michael Sioutis, Ioannis Papoutsis, Themistoklis Herekakis, Dimitrios Michail, Manolis Koubarakis and Charis Kontoes
semantic web,linked geospatial data,rdf,sparql,earth observation,fire monitoring
earth observation
semantic web
sparql
rdf
2012-11-13T15:30:00+05:00
2012-11-13T14:00:00+05:00
User Interfaces and Personalization
Weinan Zhang
9764be5858b2d4d80d06adb366635c4b9a379d47
Shanghai Jiao Tong University
Zhang
Weinan
Weinan Zhang
Van de Walle
Ghent University
209af6a5da064a4a0f0cb89a336bfeb0ebcc196d
Rik Van de Walle
Rik
Rik Van de Walle
National and Kapodistrian University of Athens
National and Kapodistrian University of Athens
Ralf Möller
48cda81324bcef6390f2117b8d3557fd5165c5ff
Technical University of Hamburg
Möller
Ralf
Ralf Möller
University of Sheffield
Stuart
Stuart Wrigley
Wrigley
Stuart Wrigley
4004916254d5dd83b00d8109bba8e7b3e6b8b422
7250d9213c1ecbe41fc0bfeab1648bb87464c3c8
Joaquim Gabarro
Universitat Politecnica de Catalunya
Gabarro
Joaquim
Joaquim Gabarro
Workshop Chair
Archana Venbakam
03eb40feca579ad0606e75b8e9ab9d040376219f
YarcData
Venbakam
Archana
Archana Venbakam
Abir Qasem
Bridgewater College
Qasem
Abir
Abir Qasem
Erik Mannens
0fb8b8ec11b797cb181f04f8c7534a39dd42812c
Ghent University
Mannens
Erik
Erik Mannens
The Linked Data Visualization Model
The Linked Data Visualization Model
Visualization,Interaction,Semantic Web,Linked Data
The potential of the semantic data available in the Web is enormous but in most cases it is very difficult for users to explore and use this data. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. We devise a formal Linked Data Visualization model (LDVM), which allows to dynamically connect data with visualizations.
The Linked Data Visualization Model
Linked Data
Interaction
Semantic Web
Visualization
Linked Data
Interaction
Visualization
Josep Maria Brunetti Fernández, Sören Auer and Roberto Garcia
Semantic Web
Kelly Reynolds
a47e5aefc4755f4f150b16741fb248eb1bde8daf
Lehigh University
Reynolds
Kelly
Kelly Reynolds
3
1
Xingjian Zhang
a50f4191e3e209b66518184da03845c8a3a4a475
Lehigh University
Zhang
Xingjian
Xingjian Zhang
PUC-Rio
PUC-Rio
Evren
199129950aa1a391f5e9ca3fce00a2e4c17f678c
Evren Sirin
Clark & Parsia
Evren Sirin
36150f32d9f306f19c88d0edbbcda7371d9d881d
Sirin
LIPN - UMR 7030 Universit Paris 13 - CNRS
LIPN - UMR 7030 Universit Paris 13 - CNRS
Patrick
Patrick Rodler
870381a179fb4188d537171d1ffc51f34afe00f1
3251c04c1eb11ec29b744fd57506f627ed8c51fa
Alpen-Adria Universität
Patrick Rodler
Rodler
Thompson
Bryan
Bryan Thompson
Bryan Thompson
Systap
Bryan Thompson is the co-founder and Chief Scientist of SYSTAP, LLC and the lead architect of the bigdata® database platform. He is a visionary and entrepreneur working on cutting edge efforts in web architecture, the semantic web, machine learning, natural language processing, artificial intelligence, cognitive modeling, and decision- support systems. He believes that web scale graph databases, GPU accelerated graph processing, and Web 2.0 authoring models will make it possible to capture metadata about the relationships between evidence and conclusions within and across communities and offer services and user experiences that encourage and facilitate collaboration across communities when their areas of expertise touch on shared concerns. Mr. Thompson is a National Merit Scholar. He has been recognized for his contributions under the Small Business Innovation Research (SBIR) program and by the Federal Semantic Interoperability Community of Practice (SICoP). He recently presented at the prestigious Schloss Dagstuhl Seminar on Semantic Data Management. He is a past member of the W3C Advisory Committee and participated the standardization efforts for SPARQL 1.0, Web Services Architecture, and XML Topic Maps.
University of Sheffield
University of Sheffield
Russell Newman
846ee9dd77f1c1746725f5bd92ea56beac3d28fc
University of Southampton
Newman
Russell
Russell Newman
Corlosquet
Massachusetts General Hospital
Stéphane Corlosquet
Stéphane Corlosquet
2e080186a0f0d5d34fb46f01d76a4813b8137a76
Stéphane
Miel Vander Sande
9da436ca83fe1f6c31b15af200be0fee8e29faca
Ghent University
Vander Sande
Miel
Miel Vander Sande
heterogeneous ontology
query translation
Heterogeneity of ontologies on the web of data is very important problem. To solve this problem, there are a lot of researches about ontology mapping/alignment/matching. This paper shows an application called SPARQLoid that is using a query rewriting method to enable the users to query any SPARQL endpoint with the users own ontology even when their mappings are not reliable enough. Often ontology matching is very difficult problem and it sometimes produces mappings under a certain reliability. Based on the given reliability degrees on those mappings, SPARQLoid allows users to query data in the target SPARQL endpoints by using their own (or a specified certain) ontology under a control of sorting order based on their mapping reliability.
SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability
SPARQL
SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability
SPARQL,heterogeneous ontology,query translation
heterogeneous ontology
query translation
SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability
SPARQL
Takahisa Fujino and Naoki Fukuta
Consuming
Reasoning
The quantity of published Linked Data is increasing dramatically. However, applications that consume Linked Data are not yet widespread. Current approaches lack methods for seamless integration of Linked Data from multiple sources, dynamic discovery of available data and data sources, provenance and information quality assessment, application development environments, and appropriate end user interfaces. Addressing these issues requires well-founded research, including the development and investigation of concepts that can be applied in systems which consume Linked Data from the Web. Following the success of the 1st and the 2nd International Workshop on Consuming Linked Data, we organize the third edition of this workshop in order to provide a platform for discussion and work on these open research problems. The main objective is to provide a venue for scientific discourse including systematic analysis and rigorous evaluation of concepts, algorithms and approaches for consuming Linked Data.
Consuming
Reasoning
Linked Data
Data Management
COLD-2012
Linked Data
Search
Query Processing
Data Management
Search
2012-11-12T09:00:00+05:00
Query Processing
2012-11-12T17:30:00+05:00
Linked Data, Consuming, Data Management, Query Processing, Search, Reasoning
Third International Workshop on Consuming Linked Data
Machine Perception
Semantic Sensor Web
The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception - explanation and discrimination - and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.
Sensor Data
76490145
An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices
An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices
Semantic Sensor Web
Machine Perception
Resource-Constrained Environments
Machine Perception, Semantic Sensor Web, Sensor Data, Mobile Device, Resource-Constrained Environments
Mobile Device
An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices
Sensor Data
76490145
Mobile Device
Cory Henson, Krishnaprasad Thirunarayan and Amit Sheth
Resource-Constrained Environments
2012-11-13T11:00:00+05:00
Description Logic
2012-11-13T12:30:00+05:00
Yasuhiro Fujiwara
487e2f0e0d87252bba38742b712f6a316a23edc8
NTT
Fujiwara
Yasuhiro
Yasuhiro Fujiwara
tracing
manufacturing process
Fairtrace - Tracing the textile industry
manufacturing process
business process
business process
tracing
Fairtrace - Tracing the textile industry
Fairtrace - Tracing the textile industry
Bruno Alves and Michael Schumacher
manufacturing process,tracing,business process
Giusy Di Lorenzo
d309a68d87c0f4cba5dffb937a40866e811f87eb
IBM Research
Di Lorenzo
Giusy
Giusy Di Lorenzo
ISWC2012 Session Chair
École des Mines de Saint-Étienne
École des Mines de Saint-Étienne
Mike Dean
Dean
Mike Dean
6006a914f7d645142fadf08d9a4e33aee98416d0
Mike
Raytheon BBN Technologies
Michael Uschold
dec13295cf321695245e9fce5dc7648f0ecaf7b6
Reinvent Technology
Uschold
Michael
Michael Uschold
White Hill
TasLab, Informatica Trentina SpA
6fc18ceccfe31ac7c9d76fea4e28a96b6fdd804e
Pavel Shvaiko
Pavel
Shvaiko
Pavel Shvaiko
Universidad Autonoma de Madrid
Universidad Autonoma de Madrid
St. James
DERI, NUI Galway
DERI, NUI Galway
Marieke Van Erp
Marieke Van Erp
VU Amsterdam
f4e16d18528b83fd8b91b603583cbfd8d15f30f2
Marieke
Van Erp
Giulia Masotti
cfefdd0c8db7aaf05f1aac06f72fa7575f7bca2c
Sapienza Università di Roma
Masotti
Giulia
Giulia Masotti
Peter Mika
Yahoo! Research
c0d6551197a0295bfc604841a994d544e0091665
Mika
Peter
Peter Mika
Olivier Curé
Olivier Curé
33cd62b318bd504dde9eea1cdcf0095394e4ba40
Olivier
Université Paris-Est LIGM
Curé
Yahoo! Research Silicon Valley
Yahoo! Research Silicon Valley
76500454
Thomas Malone
The Semantic Web and Collective Intelligence
The original vision of the Semantic Web was to encode semantic content on the web in a form with which machines can reason. But in the last few years, we've seen many new Internet-based applications (such as Wikipedia, Linux, and prediction markets) where the key reasoning is done, not by machines, but by large groups of people. This talk will show how a relatively small set of design patterns can help understand a wide variety of these examples. Each design pattern is useful in different conditions, and the patterns can be combined in different ways to create different kinds of collective intelligence. Building on this foundation, the talk will consider how the Semantic Web might contribute to - and benefit from - these more human-intensive forms of collective intelligence.
The Semantic Web and Collective Intelligence
76500454
The Semantic Web and Collective Intelligence
Fact Validation,Information Retrieval,Information Extraction,RDF,DBpedia
One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation) - an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.
RDF
Fact Validation
Jens Lehmann, Daniel Gerber, Mohamed Morsey and Axel-Cyrille Ngonga Ngomo
DeFacto - Deep Fact Validation
76490305
DeFacto - Deep Fact Validation
Information Extraction
Information Retrieval
Information Retrieval
DBpedia
DBpedia
spotlight
Fact Validation
DeFacto - Deep Fact Validation
76490305
Information Extraction
RDF
Alpen-Adria Universität
Alpen-Adria Universität
Souripriya Das
718cc70f8db224021b37eccce82b26647ed7f935
Oracle
Das
Souripriya
Souripriya Das
IT-BHU
IT-BHU
ISWC2012 Session Chair
Philippe Cudré-Mauroux, Jeff Heflin, Evren Sirin, Tania Tudorache, Jérôme Euzenat, Manfred Hauswirth, Josiane Xavier Parreira, Jim Hendler, Guus Schreiber, Abraham Bernstein, Eva Blomqvist
Proceedings of 11th International Semantic Web Conference (ISWC2012), November 11 - November 15, 2012
2012
Nov
Proceedings of ISWC 2012
Mark Greaves
5bcf15a284a0794ebad572e885d9e856055c46e0
Vulcan Inc
Greaves
Mark
Mark Greaves
2012-11-12T17:30:00+05:00
The ISWC 2012 Doctoral Consortium will take place as part of the 11th International Semantic Web Conference in Boston, US. This forum will provide PhD students an opportunity to share and develop their research ideas in a critical but supportive environment, get feedback from mentors who are senior members of the Semantic Web research community, explore issues related to academic and research careers, and build relationships with other Semantic Web PhD students from around the world. The Consortium aims to broaden the perspectives and improve the research and communication skills of these students as a way to contribute both to the individuals as well as to the broader research community.
Doctoral Consortium
2012-11-12T09:00:00+05:00
Volker Haarslev
Concordia University
Haarslev
Volker
Volker Haarslev
Berlin Institute of Technology
Berlin Institute of Technology
Sebastian Rudolph
AIFB, Karlsruhe Institute of Technology
Rudolph
Sebastian
Sebastian Rudolph
Emily Merrill
d0f1d1886458f69e26e77317ae79931f665a7fbc
Massachusetts General Hospital
Merrill
Emily
Emily Merrill
University of Mannheim
Heiner
Heiner Stuckenschmidt
bf40527b81390e320fabaea1845c7eb337f7f813
Stuckenschmidt
Heiner Stuckenschmidt
Universidad de Zaragoza
Universidad de Zaragoza
Create-Net
Create-Net
Anastasios
Anastasios Kementsietsidis
e6563946b9127806c6a94706e6181d5ff2eafc7e
Anastasios Kementsietsidis
Kementsietsidis
IBM Research
Semantic web
Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets
Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets
Not every fact in a large semantic dataset is of interest to an application. In the Smart-Aleck project, we have designed and implemented an interestingness algorithm that filters facts and joins them to generate new facts with higher levels of interestingness. The algorithm defines different levels of interestingness based on the semantic operations involved in generating interesting facts. The application of the algorithm is a Web site that presents a new interesting fact, rendered in English, each time users visit or refresh the page. The facts are generated from an integration of over half a billion triples from large semantic datasets including YAGO, Dbpedia, DataHub and Timbl. The uniqueness of the Smart-Aleck algorithm lies in its ability not merely to select interesting facts from the datasets but to generate new facts by joining two or more facts, possibly from different sources, by applying several comparison, chaining, grouping, aggregation and quantification operations on RDF triples. The implementation of Smart-Aleck on the web site is useful to everyone on the net to satisfy their curiosity, acquire general knowledge and design quizzes. It also has business potential as a feed for fact-of-the-day applications on cell phones and tablets.
Interestingness,Fact generation,Semantic web,Algorithm,Dataset
Fact generation
Kavi Mahesh and Pallavi Karanth
Algorithm
Fact generation
Algorithm
Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets
Semantic web
Interestingness
Dataset
Interestingness
Dataset
76490481
An increasing amount of data is published and consumed on the Web according to the Linked Data paradigm. In consideration of both publishers and consumers, the temporal dimension of data is important. In this paper we investigate the characterisation and availability of temporal information in Linked Data at large scale. Based on an abstract definition of temporal information we conduct experiments to evaluate the availability of such information using the data from the 2011 Billion Triple Challenge (BTC) dataset. Focusing in particular on the representation of temporal meta-information, i.e., temporal information associated with RDF statements and graphs, we investigate the approaches proposed in the literature, performing both a quantitative and a qualitative analysis and proposing guidelines for data consumers and publishers. Our experiments show that the amount of temporal information available in the LOD cloud is still very small; several different models have been used on different datasets, with a prevalence of approaches based on the annotation of RDF documents.
temporal information, temporal annotation, linked data
temporal annotation
temporal annotation
temporal information
76490481
linked data
temporal information
On the Diversity and Availability of Temporal Information in Linked Open Data
On the Diversity and Availability of Temporal Information in Linked Open Data
linked data
Anisa Rula, Palmonari Matteo, Andreas Harth, Steffen Stadtmüller and Andrea Maurino
On the Diversity and Availability of Temporal Information in Linked Open Data
google knowledge graph
knowledge graph
chrome extensions
google
Thomas Steiner, Ruben Verborgh, Raphaël Troncy, Joaquim Gabarro and Rik Van de Walle
google knowledge graph
Adding Realtime Coverage to the Google Knowledge Graph
Adding Realtime Coverage to the Google Knowledge Graph
In May 2012, the Web search engine Google has introduced the so-called Knowledge Graph, a graph that understands real-world entities and their relationships to one another. Entities covered by the Knowledge Graph include landmarks, celebrities, cities, sports teams, buildings, movies, celestial objects, works of art, and more. The graph enhances Google search in three main ways: by disambiguation of search queries, by search log-based summarization of key facts, and by explorative search suggestions. With this paper, we suggest a fourth way of enhancing Web search: through the addition of realtime coverage of what people say about real-world entities on social networks. We report on a browser extension that seamlessly adds relevant microposts from the social networking sites Google+, Facebook, and Twitter in form of a panel to Knowledge Graph entities. In a true Linked Data fashion, we interlink detected concepts in microposts with Freebase entities, and evaluate our approach for both relevancy and usefulness. The extension is freely available, we invite the reader to reconstruct the examples of this paper to see how realtime opinions may have changed since time of writing.
google knowledge graph,knowledge graph,social networks,chrome extensions,google
knowledge graph
google
chrome extensions
Adding Realtime Coverage to the Google Knowledge Graph
social networks
social networks
Hong Kong University of Science and Technology
Hong Kong University of Science and Technology
Sebastian Walter
90660a9e6019c34387bd44962645df91bb829f9d
Bielefeld University
Walter
Sebastian
Sebastian Walter
University of Southampton
dbac203aa2b30d8966a8988ead8eaaae73ea924f
Mike Wald
Wald
Mike
Mike Wald
SPARQL
76490609
Why-provenance
Provenance for SPARQL queries
m-semirings
Provenance for SPARQL queries
Provenance for SPARQL queries
Why-provenance,SPARQL,m-semirings,Difference
Carlos Viegas Damásio, Anastasia Analyti and Grigoris Antoniou
SPARQL
Difference
Difference
m-semirings
Why-provenance
76490609
Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.
1
Harokopio University of Athens
fa896e051199b582f635ce6817c3f93d75365d55
Dimitrios
Dimitrios Michail
Dimitrios Michail
Michail
Victor de Boer
a56410ed7ee59d7010ab04961a0f9409fa78fa89
VU Amsterdam
de Boer
Victor
Victor de Boer
Roberto Garcia
f392e3ebd35b26a8964e824ebd8324623bfb57fb
Universitat de Lleida
Garcia
Roberto
Roberto Garcia
VU Amsterdam
VU Amsterdam
Statler
University of Bremen
University of Bremen
Thomas Bosch
d0d8009527476b79beb8f8736933263cac907aa1
GESIS - Leibniz Institute for the Social Sciences
Bosch
Thomas
Thomas Bosch
Direct Mapping
In this demo we introduce Quest, a new system that provides SPARQL query answering with support for OWL~2~QL and RDFS entailments. Quest allows to link the vocabulary of an ontology to the content of a relational database through mapping axioms. These are then used together with the ontology to answer a SPARQL query by means of a single SQL query that is executed over the database. Quest uses highly-optimised query rewriting techniques to generate the SQL query which not only takes into account the entailments of the ontology and data, but is also 'lean' and simple so that it can be executed efficiently by any SQL engine. Quest supports commercial and open source databases, including database federation tools like Teiid to allow for Ontology Based Data Integration of relational and other sources (e.g., CSV, Excel, XML). Here we will briefly describe Quest mapping language, the query answering process and the most relevant optimisation techniques used by the system. We will conclude with a brief description of the content of this demo.
Movie Ontology
Direct Mapping
R2RML
SPARQL
Movie Ontology
SPARQL
RDBMS
RDF
RDFS
D2RQ
OWL 2
RDB2RDF
Quest: Efficient SPARQL-to-SQL for RDF and OWL
SQL
OWL 2
D2RQ
RDB2RDF,R2RML,Direct Mapping,SPARQL,SQL,DBMS,RDBMS,RDF,RDFS,OWL 2,OWL 2 QL,IMDB,Movie Ontology,D2RQ
RDFS
RDBMS
OWL 2 QL
R2RML
IMDB
OWL 2 QL
IMDB
Quest: Efficient SPARQL-to-SQL for RDF and OWL
DBMS
Quest: Efficient SPARQL-to-SQL for RDF and OWL
SQL
Mariano Rodriguez-Muro, Josef Hardi and Diego Calvanese
RDB2RDF
DBMS
RDF
ISWC2012 Session Chair
Aibo Tian
8e6e08bd78f610033fcb23b4004d6022fe158a1b
University of Texas at Austin
Tian
Aibo
Aibo Tian
Philippe Cudré-Mauroux
Philippe Cudré-Mauroux
cd093dfee55f5ac86ec10c79157bff2ad1107ec9
Cudré-Mauroux
Philippe
University of Fribourg
Tom
1dd0dab717be578c153b8ed70bee284845439706
Tom Heath
Tom Heath
Heath
Talis
Yue Ma
82ef74612bb2089432ad12fdaa528dc67857361c
LIPN - UMR 7030 Universit Paris 13 - CNRS
Ma
Yue
Yue Ma
DERI, NUI Galway
Giovanni Tummarello
90731dc3a8b2dfda68b0eeedfb1176c6bd494bfe
Giovanni Tummarello
Giovanni
Tummarello
76490513
Microtask
Microtask
Ontology alignment
Cristina Sarasua, Elena Simperl and Natasha F. Noy
CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks
The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CROWDMAP, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CROWDMAP translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CROWDMAP in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner.
76490513
CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks
Crowdsourcing
Ontology alignment,Microtask,Crowdsourcing
CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks
Ontology alignment
Crowdsourcing
Christian Meilicke
aa10dcc1abe225b12ac6c62c75224109957f8837
University of Mannheim
Meilicke
Christian
Christian Meilicke
ISWC2012 Session Chair
REST
biomass
SPARQL
linked data
LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain
algae
biomass
ontologies
LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain
REST
SPARQL
ontologies
linked data
algae
LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain
Monika Solanki
biomass,algae,linked data,ontologies,REST,SPARQL
In this paper we present, LEAPS, a Semantic Web and Linked data framework for searching and visualising datasets from the domain of Algal biomass. LEAPS provides tailored interfaces to explore algal biomass datasets via REST services and a SPARQL endpoint for stakeholders in the domain of algal biomass. The rich suite of datasets include data about potential algal biomass cultivation sites, sources of CO2, the pipelines connecting the cultivation sites to the CO2 sources and a subset of the biological taxonomy of algae derived from the world's largest online information source on algae.
Jinhyung Kim
df4307372f71608e923e96708637b058e3f0dd7e
Korea Institute of Science and Technology Information
Kim
Jinhyung
Jinhyung Kim
University of Modena and Reggio Emilia
University of Modena and Reggio Emilia
Anisa Rula
b7618420d4c418a900f5f4cb4f8294f78fae2b47
University of Milano-Bicocca
Rula
Anisa
Anisa Rula
Linked Data
Semantic Web
Schema Matching
Semantic Web
Schema Matching
Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing
In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b) efficiently match a large number of type pairs. We propose the use of state-of-the art locality-sensitive hashing (LSH) techniques to vastly improve the scalability of instance matching across multiple types. We show the feasibility of our approach with DBpedia and Freebase, two different type systems with hundreds and thousands of types, respectively. We describe how these techniques can be used to estimate containment or equivalence relations between two type systems, and we compare two different LSH techniques for computing instance similarity.
Linked Data
76490048
Ontology Alignment, Schema Matching, Linked Data, Semantic Web
Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing
76490048
Songyun Duan, Achille Fokoue, Oktie Hassanzadeh, Anastasios Kementsietsidis, Kavitha Srinivas and Michael Ward
Ontology Alignment
Ontology Alignment
Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing
University of Queensland
University of Queensland
3
Tackling Climate Change: Unfinished Business from the Last "Winter"
Tackling Climate Change: Unfinished Business from the Last "Winter"
76500456
76500456
In the 1990s, as the World Wide Web became not only world wide but also dense and ubiquitous, workers in the artificial intelligence community were drawn to the possibility that the Web could provide the foundation for a new kind of AI. Having survived the AI Winter of the 1980s, the opportunities that they saw in the largest, most interconnected computing platform imaginable were obviously compelling. With the subsequent success of the Semantic Web, however, our community seems to have stopped talking about many of the issues that researchers believe led to the AI Winter in the first place: the cognitive challenges in debugging and maintaining complex systems, the drift in the meanings ascribed to symbols, the situated nature of knowledge, the fundamental difficulty of creating robust models. These challenges are still with us; we cannot wish them away with appeals to the open-world assumption or to the law of large numbers. Embracing these challenges will allow us to expand the scope of our science and our practice, and will help to bring us closer to the ultimate vision of the Semantic Web.
Tackling Climate Change: Unfinished Business from the Last "Winter"
Mark A. Musen
Bert van Nuffelen
663f68e05ac0f7eb0997737bd0b6d54aea1d012f
Tenforce
van Nuffelen
Bert
Bert van Nuffelen
DFKI
DFKI
W3C
W3C
Charis Kontoes
8c9f34c7ae4eaf63c5b0ba473eae819206215c9a
National Observatory of Athens
Kontoes
Charis
Charis Kontoes
Hugh
University of Southampton
Glaser
Hugh Glaser
623018b35a1850179ed2903e332d96978eeb1d4f
Hugh Glaser
Patel-Schneider
Nuance Communications
35a838a13f014e0d3924f7a0aeeb929105fbf234
Peter Patel-Schneider
Peter
Peter Patel-Schneider
ISWC2012 Session Chair
Carlo Allocca
008147d42b7fef2e6877d91208558dc98b720422
KMi, The Open University
Allocca
Carlo
Carlo Allocca
Birte Glimm
University of Ulm
Birte Glimm
Birte
Glimm
d9e3004543dab6b7586ec0c3846985b999320232
David De Roure
University of Oxford
Roure
David
David De Roure
Sebastian Krause
c47cf68971d68146ed0b1f7f382926f287fd8de5
DFKI
Krause
Sebastian
Sebastian Krause
Raytheon BBN Technologies
Raytheon BBN Technologies
Amit
55747506d0fd11467524803c0f0d390d8b9e79ad
Sheth
Amit Sheth
Kno.e.sis Center, Wright State University
Amit Sheth
c903202d3919813029e4dc56efbe0a2b2443074c
L3S Research Center
L3S Research Center
76490497
Sentiment analysis
76490497
Semantic Sentiment Analysis of Twitter
Hassan Saif, Yulan He and Harith Alani
Semantic Sentiment Analysis of Twitter
Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. ''Apple product'') as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.
semantic concepts
feature interpolation.
Semantic Sentiment Analysis of Twitter
Sentiment analysis
Sentiment analysis, semantic concepts, feature interpolation.
feature interpolation.
semantic concepts
Toshio Uchiyama
1001d9a99745d35b47886955f9009e2802134293
NTT
Uchiyama
Toshio
Toshio Uchiyama
Konstantina Bereta
Bereta
Konstantina
Konstantina Bereta
6dc2e6c44ba12728237ec901f3f5190970a8dbf3
National and Kapodistrian University of Athens
3
DEQA: Deep Web Extraction for Question Answering
Question Answering
DEQA: Deep Web Extraction for Question Answering
Natural Language Processing
Jens Lehmann, Tim Furche, Giovanni Grasso, Axel-Cyrille Ngonga Ngomo, Christian Schallhart, Andrew Sellers, Christina Unger, Lorenz Bühmann, Daniel Gerber, Konrad Höffner, David Liu and Sören Auer
Web Extraction
Despite decades of effort, intelligent object search remains elusive. Neither search engine nor semantic web technologies alone have managed to provide usable systems for simple questions such as "Find me a flat with a garden and more than two bedrooms near a supermarket." We introduce DEQA, a conceptual framework that achieves this elusive goal through combining state-of-the-art semantic technologies with effective data extraction. To that end, we apply DEQA to the UK real estate domain and show that it can answer a significant percentage of such questions correctly. DEQA achieves this by mapping natural language questions to SPARQL patterns. These patterns are then evaluated on an RDF database of current real estate offers. The offers are obtained using OXPATH, a state-of-the-art data extraction system, on the major agencies in the Oxford area and linked through LIMES to background knowledge such as the location of supermarkets.
Linking
Question Answering
SPARQL
Web Extraction
Natural Language Processing
Question Answering,Web Extraction,Linking,Conceptual Framework,Natural Language Processing,SPARQL
DEQA: Deep Web Extraction for Question Answering
Conceptual Framework
76500129
Conceptual Framework
76500129
SPARQL
Linking
Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures
Linked Data
Link Discovery,Linked Data,Reduction Ratio
Reduction Ratio
Axel-Cyrille Ngonga Ngomo
Reduction Ratio
Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures
Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures
76490369
Link Discovery
Link Discovery
Linked Data
76490369
Time-efficient algorithms are essential to address the complex linking tasks that arise when trying to discover links on the Web of Data. Although several lossless approaches have been developed for this exact purpose, they do not offer theoretical guarantees with respect to their performance. In this paper, we address this drawback by presenting the first Link Discovery approach with theoretical quality guarantees. In particular, we prove that given an achievable reduction ratio r, our Link Discovery approach HR3 can achieve a reduction ratio r'<=r in a metric space where distances are measured by the means of a Minkowski metric of any order p >= 2. We compare HR3 and the HYPPO algorithm implemented in LIMES 0.5 with respect to the number of comparisons they carry out. In addition, we compare our approach with the algorithms implemented in the state-of-the-art frameworks LIMES 0.5 and SILK 2.5 with respect to runtime. We show that HR3 outperforms these previous approaches with respect to runtime in each of our four experimental setups.
Thanos G. Stavropoulos, Dimitris Vrakas and Ioannis Vlahavas
Semantic Web,Web Services,Semantic Web Services,Tools
Although the Semantic Web and Web Service technologies have already formed a synergy towards Semantic Web Services, their use remains limited. Potential adopters are usually discouraged by the plurality of methodologies and the lack of tools which in turn force them to acquire expert knowledge and commit to exhausting manual labor. This work proposes a novel, functional and user-friendly tool, named Iridescent, intended for both expert and non-expert users to rapidly create and edit Semantic Web Service descriptions, following the SAWSDL recommendation. The tools aim is twofold: to enable users manually create descriptions in a visual manner, providing a complete alternative to coding, and to semi-automate the process by matching elements and concepts and suggesting annotations. A state-of-the-art survey has been carried out to reveal critical requirements and compare Iridescent to existing tools. Usage scenarios demonstrate how Iridescent enhances the authoring process and in turn enables Intelligence e.g. in an Ambient Intelligence environment. Finally, the tool was methodically tested for usability and evaluated by a range of expert and non-expert users.
Semantic Web Services
Semantic Web
Iridescent: a Tool for Rapid Semantic Web Service Descriptions
Web Services
Semantic Web
Iridescent: a Tool for Rapid Semantic Web Service Descriptions
Tools
Tools
Iridescent: a Tool for Rapid Semantic Web Service Descriptions
Semantic Web Services
Web Services
Markus Luczak-Rösch
94a7f6b90cec414ecc1b1d92f07a4d0eac6da5a0
Free University of Berlin
Luczak-Rösch
Markus
Markus Luczak-Rösch
Andrea Maurino
b169d9fa8bee3225df614c24b18db7a171010ee8
University of Milano-Bicocca
Maurino
Andrea
Andrea Maurino
RPI
RPI
Jeff
Jeff Z. Pan
University of Aberdeen
e409b5eceff3b8cf4be69005301c6984fa6ceae3
Jeff Z. Pan
Pan
Stanford University
Natasha
Natasha F. Noy
7d8b34490f91ef43b84489a7630394b04a8c2c21
Natasha F. Noy
Noy
Matthias Nickles
Matthias
Matthias Nickles
Technical University of Munich
Nickles
9458c32cbaab2efbd347ca14440cbd68a9647114
KMi, The Open University
KMi, The Open University
c7ab3ec57f6237d69228d71e804b910f47eee0fb
Ravindra
Padmashree Ravindra
Padmashree Ravindra
Padmashree
North Carolina State University
Machine learning
A Machine Learning Approach for Instance Matching Based on Similarity Metrics
Machine learning
Linking Open Data
Shu Rong, Xing Niu, Evan Wei Xiang, Haofen Wang, Qiang Yang and Yong Yu
Instance matching
A Machine Learning Approach for Instance Matching Based on Similarity Metrics
76490449
Transfer learning
A Machine Learning Approach for Instance Matching Based on Similarity Metrics
Linking Open Data,Instance matching,Similarity matric,Machine learning,Transfer learning
Similarity matric
Linking Open Data
Instance matching
Transfer learning
The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.
Similarity matric
76490449
2
Driving Innovation with Open Data and Interoperability
Driving Innovation with Open Data and Interoperability
76500455
76500455
Data.gov, a flagship open government project from the US government, opens and shares data to improve government efficiency and drive innovation. Sharing such data allows us to make rich comparisons that could never be made before and helps us to better understand the data and support decision making. The adoption of open linked data, vocabularies and ontologies, the work of the W3C, and semantic technologies is helping to drive Data.gov and US data forward. This session will help us to better understand the changing global landscape of data sharing and the role the semantic web is playing in it. This session highlights specific data sharing examples of solving mission problems from NASA, the White House, and many other governments agencies and citizen innovators.
Jeanne Holm
Driving Innovation with Open Data and Interoperability
Garlik
Garlik
ISWC2012 Session Chair
Rutgers University
Rutgers University
Auer
09ac456515dee0896e8eba4b06ae589bef2069cf
Sören Auer
Sören Auer
Sören
TU Chemnitz
Alpen-Adria Universität
Philipp
Philipp Fleiss
Fleiss
b02bd2d523e7d1559b65f6f873c854ba9fa018c5
Philipp Fleiss
Szymon Klarman
2e6a9d20cccc1f31cf01e0cf43929a60066ea900
VU Amsterdam
Klarman
Szymon
Szymon Klarman
Kathryn Dunn
44d9c6c8c5907c48fd929430f7b1c67bbdebb741
RPI
Dunn
Kathryn
Kathryn Dunn
Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids
Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids
Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids
Semantic Web
Semantic Web,complex event processing,smart grid
76500254
Semantic Web
complex event processing
76500254
smart grid
smart grid
Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require continuous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promising to model diverse Smart Grid domain knowledge for enhanced situation awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic analysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by diverse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management.
Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna
complex event processing
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
Semantic Web
Semantic Web
UMLS
PubMed
We present iExplore, a Semantic Web based application that helps biomedical researchers study and explore biomedical knowledge interactively. iExplore uses the Biomedical Knowledge Repository (BKR), which integrates knowledge from various sources ranging from information extracted from biomedical literature (from PubMed) to many structured vocabularies in the Unified Medical Language System (UMLS). The current version of BKR provides a unified provenance representation for 12 million semantic predications (triples with a predicate connecting a subject and an object) derived from 87 vocabulary families in the UMLS and 14 million predications extracted from 21 million PubMed abstracts. To engage the domain experts in studying and exploring such a comprehensive knowledge base, we developed the iExplore to: 1) visualize and navigate all the possible semantic predications related to concepts of interest, and 2) search for interesting links between concepts. We also provide an authorization mechanism for SPARQL queries generated by the iExplore to support licensed access to UMLS. We demonstrate the use of iExplore in two scenarios: 1) current research in biomedicine, and 2) re-exploration of two previously known literature-based discoveries. iExplore is available at http://knoesis.wright.edu/iExplore.
semantic predication
semantic predication
semantic similarity
RDF visualization
knowledge exploration
RDF visualization,UMLS,PubMed,knowledge exploration,semantic predication,semantic similarity,Semantic Web
UMLS
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
RDF visualization
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
knowledge exploration
semantic similarity
PubMed
Vinh Nguyen, Olivier Bodenreider, Jagannathan Srinivasan, Todd Minning, Thomas Rindflesch, Bastien Rance, Ramakanth Kavuluru, Hima Yalamanchili, Krishnaprasad Thirunarayan, Satya Sahoo and Amit Sheth
Christian Schallhart
f58131098ea4801c804a5537cb3d6968f68e7066
University of Oxford
Schallhart
Christian
Christian Schallhart
Cristina Sarasua
5ffe114aa7c2c48df85158f381783f55a382eb0d
AIFB, Karlsruhe Institute of Technology
Sarasua
Cristina
Cristina Sarasua
linked API
linked data
Rapidly Integrating Services into the Linked Data Cloud
service modeling
linked API
76490545
service modeling
Mohsen Taheriyan, Craig Knoblock, Pedro Szekely and José Luis Ambite
linked data
Rapidly Integrating Services into the Linked Data Cloud
76490545
linked data, linked API, service modeling
The amount of data available in the Linked Data cloud continues to grow. Yet, few services consume and produce linked data. There is recent work that allows a user to define a linked service from an online service, which includes the specifications for consuming and producing linked data, but building such models is time consuming and requires specialized knowledge of RDF and SPARQL. This paper presents a new approach that allows domain experts to rapidly create semantic models of services by demonstration in an interactive web-based interface. First, the user provides examples of the service request URLs. Then, the system automatically proposes a service model the user can refine interactively. Finally, the system saves a service specification using a new expressive vocabulary that includes lowering and lifting rules. This approach empowers end users to rapidly model existing services and immediately use them to consume and produce linked data.
Rapidly Integrating Services into the Linked Data Cloud
Giovanni Grasso
1b061673dafcb1e36b8d5aebf4a79f9921f6be8a
University of Oxford
Grasso
Giovanni
Giovanni Grasso
query processing
Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig
76490241
Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig
Spyros Kotoulas, Jacopo Urbani, Peter Boncz and Peter Mika
Map-Reduce
Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig
SPARQL,query processing,Map-Reduce
SPARQL
We describe a system that incrementally translates SPARQL queries to Pig Latin and executes them on a Hadoop cluster. This system is designed to work efficiently on complex queries with many self-joins over huge datasets, avoiding job failures even in the case of joins with unexpected high-value skew. To be robust against cost estimation errors, our system interleaves query optimization with query execution, determining the next steps to take based on data samples and statistics gathered during the previous step. Furthermore, we have developed a novel skew-resistant join algorithm that replicates tuples corresponding to popular keys. We evaluate the effectiveness of our approach both on a synthetic benchmark known to generate complex queries (BSBM-BI) as well as on a Yahoo! case of data analysis using RDF data crawled from the web. Our results indicate that our system is indeed capable of processing huge datasets without pre-computed statistics while exhibiting good load-balancing properties.
Map-Reduce
SPARQL
76490241
query processing
Clarendon
Alexandra Poulovassilis
Birkbeck College, University of London
Poulovassilis
Alexandra
Alexandra Poulovassilis
The Boston Park Plaza Hotel & Towers
617.426.2000
50 Park Plaza at Arlington Street, Boston, MA 02116
617.426.5545
Located in the heart of historic Back Bay, The Boston Park Plaza Hotel & Towers is one of Boston�s most recognized and renowned landmarks. The Boston Park Plaza, a member of Historic Hotels of America, was constructed in March, 1927 as part of the E.M. Statler Empire. With an unsurpassed Boston address, the hotel is located only 3 miles from Logan International Airport and only 200 yards from the nation�s first public parks, Boston Common & the Public Garden. The hotel is easily accessible to public transportation, world renowned shopping along Newbury Street, Faneuil Hall Marketplace, the Theatre & Financial Districts and most historic landmarks. Rich in history, The Boston Park Plaza Hotel & Towers has distinguished itself with classic elegance and personalized service that continues to attract travelers from all over the world who visit Boston for business, leisure or special events.
Oracle
Oracle
23bb759762da3a685f2657adb391386b270f004e
Dimitris
Dimitris Plexousakis
University of Crete; FORTH-ICS
Dimitris Plexousakis
Plexousakis
3
Tom De Nies
c96075b326e3cd7a080c63b70f0832522c65ac4c
Ghent University
De Nies
Tom
Tom De Nies
YarcData
YarcData
2012-11-12T14:00:00+05:00
Financial Information Management Using the Semantic Web
Financial IM
2012-11-12T17:30:00+05:00
Recent financial crises have demonstrated the need for sophisticated modeling of and deep reasoning about financial events and associated financial information. These tasks present foundational and technical challenges which are best addressed using open standards developed by the Semantic Web community. The tutorial will explains foundational concepts in finance and semantic web technologies, present relevant standards and languages, and work through several use cases and motivating examples in detail.
University of Mynuch
University of Mynuch
c0879a5783f8750335b2d2830dd7dbb99dc8f94b
Grau
University of Oxford
Bernardo Cuenca Grau
Bernardo Cuenca Grau
Bernardo
Dimitris Vrakas
Aristotle University of Thessaloniki
Dimitris
Vrakas
bc5d468ff719b251a08617bcad321a83a09757a4
Dimitris Vrakas
bd1e3cb53855eb1dd72620052d91e85a7e483e1a
Yunjia Li
Yunjia Li
Li
University of Southampton
Yunjia
Avigdor Gal
Technion
Gal
Avigdor
Avigdor Gal
Xingjian Zhang, Dezhao Song, Sambhawa Priya, Zachary Daniels, Kelly Reynolds and Jeff Heflin
Exploring the Linked Data Cloud via Contextual Tag Cloud
Linked Open Data,Tag Cloud Browsing,Billion Triples Challenge,Semantic Web Exploration
Exploring the Linked Data Cloud via Contextual Tag Cloud
Billion Triples Challenge
Semantic Web Exploration
Tag Cloud Browsing
Exploring the Linked Data Cloud via Contextual Tag Cloud
Billion Triples Challenge
Semantic Web Exploration
Tag Cloud Browsing
Linked Open Data
Linked Open Data
We present the contextual tag cloud system, where the context defines a subset of instances, the tags are ontological terms (classes and properties), and the font sizes reflect the number of instances that use each tag. With our system, users can get familiar with the terms and understand how the dataset is populated; or they can dynamically add tags as context and investigate features or look at instances within the constrained subset. With a domain knowledge base, this system helps reveal the patterns of data. For the massive Linked Open Data cloud, it reveals the extent to which data are linked with regard to both the equivalent instances and ontology alignment. In order to achieve this function, we precompute the owl:sameAs closure, and support multiple levels of RDFS entailment. By using an inverted index and pruning algorithms, we design and implement a real time response system for the Billion Triple Challenge dataset. This submission is for the Billion Triples track in Semantic Web Challenge. Our system is available at http://gimli.cse.lehigh.edu:8080/btc/
ISWC2012 Session Chair
Linked Data
The distributed and heterogeneous nature of Linked Open Data requires flexible and federated techniques for query evaluation. In order to evaluate current federation querying approaches a general methodology for conducting benchmarks is mandatory. In this paper, we present a classification methodology for federated SPARQL queries. This methodology can be used by developers of federated querying approaches to compose a set of test benchmarks that cover diverse characteristics of different queries and allows for comparability. We further develop a heuristic called SPLODGE for automatic generation of benchmark queries that is based on this methodology and takes into account the number of sources to be queried and several complexity parameters. We evaluate the adequacy of our methodology and the query generation strategy by applying them on the 2011 billion triple challenge data set.
Olaf Görlitz, Matthias Thimm and Steffen Staab
Benchmark
76490113
RDF
Linked Data
Linked Data,SPARQL,RDF,Distributed Query Processing,Benchmark
SPARQL
SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data
SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data
SPARQL
76490113
SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data
Distributed Query Processing
Distributed Query Processing
RDF
Benchmark
Spyros
Kotoulas
Spyros Kotoulas
f8f973f695ad43bf3e13d8f97ef19f4878dfcd9d
Spyros Kotoulas
e879e287903caecdd41354eb5ae7aff6d9bc741b
IBM Research
David Liu
e41b33383ba7b27f60995dd9cc47e0f6e5c9e8ab
University of Oxford
Liu
David
David Liu
Jay Banerjee
17a34dd13931213750711667cad1dce5bfe62592
Oracle
Banerjee
Jay
Jay Banerjee
Tryfonopoulos
University of Peloponnese
3918b205494022409a1198958cc40ae1af4ccd84
Christos Tryfonopoulos
Christos Tryfonopoulos
Christos
Carlos Buil-Aranda
05aa09c1188d0bdeacc24e7824731849b88fe11c
Universidad Politecnica de Madrid
Buil-Aranda
Carlos
Carlos Buil-Aranda
Building Large Scale Relation KB from Text
Relation Extraction
Relation Extraction
Recently more and more structured data in form of RDF triples have been published and integrated into Linked Open Data (LOD). While the current LOD contains hundreds of data sources with billions of triples, it has a small number of distinct relations compared with the large number of entities. On the other hand, Web pages are growing rapidly, which results in much larger number of textual contents to be exploited. With the popularity and wide adoption of open information extraction technology, extracting entities and relations among them from text at the Web scale is possible. In this paper, we present an approach to extract the subject individuals and the object counterparts for the relations from text and determine the most appropriate domain and range as well as the most confident dependency path patterns for the given relation based on the EM algorithm. As a preliminary results, we built a knowledge base for relations extracted from Chinese encyclopedias. The experimental results show the effectiveness of our approach to extract relations with reasonable domain, range and path pattern restrictions as well as high-quality triples.
Building Large Scale Relation KB from Text
Expectation Maximization
Junfeng Pan, Haofen Wang and Yong Yu
Linked Data
Expectation Maximization
Building Large Scale Relation KB from Text
Linked Data,Relation Extraction,Expectation Maximization
Linked Data
William Hogan
16777bcdd23c216ab011065b29525a56f50679c9
UAMS
Hogan
William
William Hogan
1
Mark A. Musen
Mark A. Musen
Stanford University
Musen
Mark Musen from Stanfords Center for Biomedical Informatics research has been confirmed as the second keynote speaker for ISWC 2012. Mark conducts research related to intelligent systems, the Semantic Web, reusable ontologies and knowledge representations, and biomedical decision support. His long-standing work on a system known as Protégé has led to an open-source technology now used by thousands of developers around the world to build intelligent computer systems and new computer applications for e-science and the Semantic Web. He is known for his research on the application of intelligent computer systems to assist health-care workers in guideline- directed therapy and in management of clinical trials. He is principal investigator of the National Center for Biomedical Ontology, one of the seven National Centers for Biomedical Computing supported by the Roadmap of the U.S. National Institutes of Health.
Mark
60602b405f9d456ffc0e30d22d535100159b7874
Tomi Kauppinen
University of Münster
Kauppinen
08d31d978b13dc399e87221867af54d2b33c7830
Tomi
Tomi Kauppinen
Shilpa Arora
8e49167ae5c7dd76690d680c21ecf561b75de7a3
CMU
Arora
Shilpa
Shilpa Arora
3
Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale
retrieval
Thomas Gottron, Ansgar Scherp, Bastian Krayer and Arne Peters
schema based LOD discovery
schema index
user support
semantic search
schema index
retrieval,schema index,schema based LOD discovery,user support,semantic search
schema based LOD discovery
semantic search
Searching for Linked Open Data (LOD) has yet not reached the easiness and comfort we are accustomed with when using document search engines such as Google. To get closer to this "Google feeling" when searching for LOD, we have developed LODatio. Our system supports various kinds of queries on LOD such as searching for LOD sources containing specific types, properties, sets of types and properties, as well as their relations. The LOD sources are ranked along with the number of instances found that match the query. In addition, LODatio provides Google-style features such as "Did you mean?" and "Related queries" that allow the users for refining and broadening their queries.
Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale
Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale
retrieval
user support
Swiss Federal Institute for Forest, Snow and Landscape Research
Swiss Federal Institute for Forest, Snow and Landscape Research
Wells Fargo Bank
Wells Fargo Bank
University of Crete
University of Crete
provenance
web
2012-11-12T12:30:00+05:00
provenance
interchange
metadata
2012-11-12T09:00:00+05:00
provenance, interchange , web, metadata
metadata
Getting to know PROV - the W3C Provenance Specifications
provenance
interchange
Provenance (the origin or source) of information is critical in deciding whether information is to be trusted, how it should be integrated with other diverse information sources, and how to give credit to its originators when reusing it. In order to promote the widespread publication of provenance information on the Web, the W3C is producing the W3C PROV set of specifications. These specifications provide a basis for the common exchange of provenance information on the Web. This half- day tutorial provides you with an indepth dive into these specifications including handson information on how to publish, query and access provenance information. You will learn how to model your provenance data using the PROV data model and ontology, how to produce provenance information that enables integrity checking and inferences, as well as how to expose and acquire provenance information using PROV access mechanisms and services.
web
Emanuele Della Valle
Emanuele
Politecnico di Milano
1ee386235c89959195075bc4944d5c68f8265f96
Della Valle
Emanuele Della Valle
graph theory
RDFS Reasoning on Massively Parallel Hardware
Norman Heino and Jeff Z. Pan
76490129
computational geometry
RDFS Reasoning on Massively Parallel Hardware
computational geometry, graph theory, Hamilton cycles
76490129
RDFS Reasoning on Massively Parallel Hardware
Hamilton cycles
Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for fine-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can benefit even more from careful optimizations that take into account intricacies of modern parallel hardware.
graph theory
computational geometry
Hamilton cycles
Zachary Daniels
b33acc5a64c946c76e5579fb0525a8a6bbd6cba5
Lehigh University
Daniels
Zachary
Zachary Daniels
ISWC2012 Session Chair
University of Arizona
University of Arizona
Manos Karpathiotakis
National and Kapodistrian University of Athens
Manos Karpathiotakis
Karpathiotakis
Manos
9bf0e66d32e5828c334848d790c727298225d331
Hans Uszkoreit
288ac7498712bfe804d7abf3637e67947fa188e0
DFKI
Uszkoreit
Hans
Hans Uszkoreit
ISWC2012 Program Chair
George Mason University
George Mason University
2
Sequeda
Juan F. Sequeda
e36a6c5f10bf558670ec81424012f651b25e23a4
University of Texas at Austin
Juan F. Sequeda
Juan
Adila A. Krisnadhi
Kno.e.sis Center, Wright State University
Krisnadhi
Adila
Adila A. Krisnadhi
1
Patrick Siehndel and Ricardo Kawase
TwikiMe!
Wikipedia
TwikiMe! - User profiles that make sense.
TwikiMe!,Twitter,Wikipedia,Semantic User Profile
The use of social media has been rapidly increasing in the last years. Social media such as Twitter has become an important source of information for a variety of people. The public availability of data describing some of these social networks has led to a lot of research in this area. Link prediction, user classification and community detection are some of the main research areas related to social networks. In this paper we present a user modeling framework that uses Wikipedia as a frame to model user interests inside a social network. Our fine grained model of user interests reflects the areas a user is interested in as well as the level of expertise a user has in a certain field.
Twitter
TwikiMe! - User profiles that make sense.
Twitter
TwikiMe!
Semantic User Profile
Semantic User Profile
TwikiMe! - User profiles that make sense.
Wikipedia
Lin Clark
d853b032b7ac97ef3c2cbd135e5d3241933d835d
Lin Clark
Lin
DERI, NUI Galway
Clark
Yannis
Yannis Tzitzikas
University of Crete; FORTH-ICS
Tzitzikas
Yannis Tzitzikas
e9f061dc9064b1669786bbbd730b5e1a035b8e0d
Stojanovic
Ljiljana
504f4841078f792ee57902392352f07cee93449e
Ljiljana Stojanovic
FZI Research Center for Information Technology
Ljiljana Stojanovic
Machine Learning for NLP
Text processing
Semantic web literals
Natural Language Processing (NLP)
LL-NLP
LL-NLP Tutorial: What to do with long literals? Ask the NLP community...
Natural Language Processing (NLP), Named entities, Machine Learning for NLP, Semantic web literals, Text processing
2012-11-11T17:30:00+05:00
Named entities
In this tutorial, we will start with the basics of natural language processing (NLP) and will explain different levels of analysis (segmentation, morphology, lexicon, syntax, semantic). We will then look at the specific tasks involved in mining sentences to extract resource description framework (RDF) triples and show the importance that each level of language analysis can play in such tasks. NLP researchers have been working on such tasks for many years and have developed many algorithms to perform part-of-speech tagging, parsing, semantic disambiguation, named entity recognition and relation extraction. We will present baseline algorithms for these tasks to show expected results from state-of-the-art research. As many algorithms use machine learning (ML) techniques, we will provide a simple introduction to the aims and general principles behind such techniques and explain why they are so important to the NLP community. We will also explore problems that arise when extracting knowledge from multiple sentences and how that is closely related to concept mapping problems, which are familiar to the semantic web community. Since fully automatic text mining is quite a difficult task, we will emphasize that semi-automatic approaches can be effective and viable solutions for facilitating the RDFization of textual data.
Semantic web literals
Machine Learning for NLP
2012-11-11T09:00:00+05:00
Text processing
Natural Language Processing (NLP)
Named entities
2
3
EURECOM
EURECOM
Alberto Musetti
a5c3dd70656898635fee387e6d0d7443127225bd
University of Bologna
Musetti
Alberto
Alberto Musetti
Berkley
University of Fribourg
University of Fribourg
Kowledge Base Rewriting
Hitting the Sweetspot: Economic Rewriting of Knowledge Bases
Description Logics,Non-standard Reasoning,Kowledge Base Rewriting,Module Extraction
Description Logics
Three conflicting requirements arise in the context of knowledge base (KB) extraction: the size of the extracted KB, the size of the corresponding signature and the syntactic similarity of the extracted KB with the original one. Minimal module extraction and uniform interpolation assign an absolute priority to one of these requirements, thereby limiting the possibilities to influence the other two. We propose a novel technique for EL that does not require such an extreme prioritization. We propose a tractable rewriting approach and empirically compare the technique with existing approaches with encouraging results.
Nadeschda Nikitina and Birte Glimm
76490385
Module Extraction
Non-standard Reasoning
Description Logics
Kowledge Base Rewriting
Hitting the Sweetspot: Economic Rewriting of Knowledge Bases
Non-standard Reasoning
Hitting the Sweetspot: Economic Rewriting of Knowledge Bases
76490385
Module Extraction
a4ba6adf3f4b9a9497aba7a6695f1554960435d3
Pouchard
Line Pouchard
Line
Line Pouchard
Oak Ridge National Laboratory
ISWC2012 Session Chair
ISWC2012 Local Chair
Federated and Stream Query Processing
2012-11-13T17:30:00+05:00
2012-11-13T16:00:00+05:00
Michael Schumacher
9508203bd1b9a2206c108aaf0672f654e17670a5
University of Applied Sciences Western Switzerland
Schumacher
Michael
Michael Schumacher
Seppo Törmä
f3983b4836d4d9f5c3a2ca2c2d9dc30a06c2c718
Aalto University
Törmä
Seppo
Seppo Törmä
Rehab Albeladi
c335243fcf4c7309810510cd86c03da1d4e67387
University of Southampton
Albeladi
Rehab
Rehab Albeladi
3
This paper introduces a Linked Data application for automatically generating a story between two concepts in the Web of Data, based on formally described links. A path between two concepts is obtained by querying multiple linked open datasets; the path is then enriched with multimedia presentation material for each node in order to obtain a full multimedia presentation of the found path.
Linked Data,Path retrieval,Multimedia
Path retrieval
Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts
Path retrieval
Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts
Miel Vander Sande, Ruben Verborgh, Sam Coppens, Tom De Nies, Pedro Debevere, Laurens De Vocht, Pieterjan De Potter, Davy Van Deursen, Erik Mannens and Rik Van de Walle
Linked Data
Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts
Linked Data
Multimedia
Multimedia
INRIA & LIG
INRIA & LIG
University of Economics, Prague
University of Economics, Prague
Mirko Graziosi
51beb8b97e106eeaf5401d4313885d9dc079e611
Sapienza Università di Roma
Graziosi
Mirko
Mirko Graziosi
University of Maryland Baltimore County
University of Maryland Baltimore County
Christopher Matheus
Bell Labs Research
Matheus
Christopher
Christopher Matheus
1
2012-11-12T12:30:00+05:00
A Hands-on Introduction to the GoodRelations Ontology, Schema.org, RDFa and Microdata Authoring, Google Rich Snippets for Products, Yahoo, Bing, and Linked Open Commerce. The Good Relations ontology (http://purl.org/goodrelations/) is one huge success story of applying Semantic Web technology to business challenges. In this tutorial, we will (1) give a comprehensive overview and hands-on training on the conceptual structures of the GoodRelations ontology including patterns for ownership and demand, (2) present the full tool chain for producing and consuming GoodRelations- related data, (3) explain the long-term vision of linked open commerce, (4) describe the main challenges for future research in the field, and (5) discuss advanced topics, like access control, identity and authentication (e.g. with WebID); micropayment services (like Payswarm), and data management issues from the publisher and consumer perspective.
WoD E-Commerce
2012-11-12T09:00:00+05:00
The Web of Data for E-Commerce in Brief
ISWC2012 Session Chair
Basque Country University
Basque Country University
FORTH-ICS
FORTH-ICS
University of Zurich
University of Zurich
Aidan Hogan, Emir Muñoz and Jürgen Umbrich
entity search
entity search
linked data
semantic web
LODPeas: Like peas in a LOD (cloud)
similarity
similarity
LODPeas: Like peas in a LOD (cloud)
We present LODPeas: a system for browsing entities that are found to share many things in common in an RDF dataset. The system first offers standard keyword search to locate a focus entity. Once a focus entity has been found, other entities that share a lot in common with it are displayed in a graph-based visualisation. The degree to which two entities have a lot in common---their level of concurrence---is scored by looking at attributes (property--value pairs) that they share: attributes that are shared by few other entities are given higher weight, and additional shared attributes imply a stronger score. LODPeas is designed to scale for billions of triples and is built in an (almost) entirely domain-agnostic fashion, built on top of the RDF standards themselves and not requiring any domain-specific input. Herein, we describe the functionality of LODPeas, how the system was built over the BTC'12, and discuss possible applications.
semantic web
linked data
linked data,semantic web,similarity,entity search
LODPeas: Like peas in a LOD (cloud)
Approximate Reasoning
76490081
Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them âhot spotsâ?. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.
Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies
Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies
OWL Ontologies
OWL Ontologies,Reasoner Performance Variability,Approximate Reasoning,NCBO BioPortal,Description Logics
Description Logics
NCBO BioPortal
Reasoner Performance Variability
Description Logics
OWL Ontologies
NCBO BioPortal
76490081
Approximate Reasoning
Reasoner Performance Variability
Rafael S. Gonçalves, Bijan Parsia and Ulrike Sattler
Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies
Gabriela Montoya
715cf5f8a0415885ab8deef351c3422ec23deafa
Universidad Simon Bolivar
Montoya
Gabriela
Gabriela Montoya
Politecnico di Milano
Politecnico di Milano
2
583b2ab35d1cef69e21b25a7f36ec5a36e11d31d
Fabien Gandon
INRIA
Fabien Gandon
Fabien
Gandon
2012-11-11T17:30:00+05:00
rules, ontologies, RIF, SPARQL, declarative logic programs, OWL
ontologies
OWL
SPARQL
declarative logic programs
RIF
rules
SPARQL
OWL
2012-11-11T14:00:00+05:00
Semantic Web Rules: Fundamentals, Applications, and Standards
rules
declarative logic programs
SW Rules
RIF
ontologies
The area of semantic rules is perhaps the most important frontier today for the Semantic Webs core technology and standards. Recent progress includes major initial industry standards from W3C and OMG, and fundamental advances in the underlying knowledge representation techniques in declarative logic programs, including most recently for efficient higher-order defaults with sound integration of first order logic ontologies (OWL). Recent progress also includes methods to use rules for, or with, more expressive OWL ontologies; increasing integration of rules with query /search in SPARQL and relational databases; substantive translations between heterogeneous types of commercial rule engines; development of open-source tools for inferencing and interoperability; performance benchmarking of rule systems; a wide range of emerging applications including in business, science, and trust; and accelerating industry investments / acquisitions in the technology including by integrated software companies such as Oracle, IBM, and Microsoft. This tutorial will provide a comprehensive and up-to-date introduction to these developments and to the fundamentals of the key technologies and outstanding research issues involved. It will explore example application scenarios, overall requirements and challenges, and touch upon business /social value and strategy considerations.
University of Ulm
University of Ulm
Customer experience
Customer experience
Adoption of Semantic Web technologies
Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle
Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle
Adoption of Semantic Web technologies
Semantic Web applications
Semantic Web applications
Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle
Souripriya Das, Jagannathan Srinivasan, Matthew Perry and Seema Sundara
Semantic Web applications,Adoption of Semantic Web technologies,Customer experience
Romina Spalazzese
9789fbbabfadc2d45999ced58b46ea27cdf6bed3
University of L'Aquila
Spalazzese
Romina
Romina Spalazzese
National Observatory of Athens
ccaf4efbf7ddd58fb405c68c0b67027dc49216bd
Herekakis
Themistoklis
Themistoklis Herekakis
Themistoklis Herekakis
Yan Kang
bf6dcda5b66de1b951d431cff0b721739758b08e
University of Maryland
Kang
Yan
Yan Kang
Tutorial Organizer
Tejaswini Pendurthi
b167e98e97780f32f226c471d8db763078fc70e7
UALR
Pendurthi
Tejaswini
Tejaswini Pendurthi
2012-11-13T11:00:00+05:00
Search, question answering and entity summarization
2012-11-13T12:30:00+05:00
reasoning
classification
A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.
ontology,classification,reasoning,performance,prediction,metrics
metrics
metrics
reasoning
classification
prediction
76490193
prediction
ontology
Predicting Reasoning Performance Using Ontology Metrics
spotlight
Yong-Bin Kang, Yuan-Fang Li and Shonali Krishnaswamy
Predicting Reasoning Performance Using Ontology Metrics
76490193
performance
performance
ontology
Predicting Reasoning Performance Using Ontology Metrics
University of Münster
University of Münster
Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study
Search Based Application,Mashup,Geotagging,Analytics
Analytics
Mashup
Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study
Search Based Application
Search Based Application
Geotagging
Amar Djalil Mezaour, Julien Law-To, Robert Isele, Thomas Schandl and Gerd Zechmeister
Geotagging
In the recent months, the web community observed a significant increase in the growing trend towards open data usage. While this usage is common and spread for publishing and managing public data of governments and administrations, the business side is still lacking of major concrete use cases to demonstrate the benefit of open linked data in enterprise information management. In this paper, we present "Active Hiring" a search based application on the cloud providing analytics on on-line job posts. Active Hiring application uses services from the LOD cloud to disambiguate, geotag and interlink data entities acquired from on-line job boards web sites.
Mashup
Analytics
Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study
data fusion
linked data,data fusion,data integration,data quality
data integration
linked data
As part of LOD2 project and OpenData.cz initiative, we are developing an ODCleanStore framework enabling management of Linked Data. In this paper, we focus on the query-time data fusion in ODCleanStore, which provides data consumers with integrated views on Linked Data; the fused data (1) has solved conflicts according to the preferred conflict resolution policies and (2) is accompanied with provenance and quality scores, so that the consumers can judge the usefulness and trustworthiness of the data for their task at hand.
Linked Data Fusion in ODCleanStore Framework
Jan Michelfeit and Tomá Knap
linked data
data integration
data quality
data fusion
Linked Data Fusion in ODCleanStore Framework
data quality
Linked Data Fusion in ODCleanStore Framework
3
033fde5cee235c127687c07129f1fff99761d503
Satya
Satya Sahoo
Satya Sahoo
Sahoo
Case Western Reserve University
Thomas Krennwallner
4fbf23f97eb1c36561a72379cea71095200fbd60
Vienna University of Technology
Krennwallner
Thomas
Thomas Krennwallner
Thanh Tran
AIFB, Karlsruhe Institute of Technology
Tran
Thanh
Thanh Tran
Sabou
Marta
MODUL University Vienna
Marta Sabou
Marta Sabou
0d48e252f345b200071d6faea2f171ed494fd0c7
Shanghai Jiao Tong University
Shanghai Jiao Tong University
Bernhard
Schandl
Gnowsis.com
Bernhard Schandl
079c9cf4971f05cbfee8e2bbdb5f9d9cfa777b83
Bernhard Schandl
Derivo
Derivo
Austrian Institute of Technology
Austrian Institute of Technology
Drupal
Stéphane Corlosquet, Sudeshna Das, Emily Merrill, Paolo Ciccarese and Tim Clark
JSON-LD
Drupal,biomedical,WebID,JSON-LD,RDFa,real-world deployment
biomedical
JSON-LD
Drupal as a Semantic Web platform
Drupal
Drupal as a Semantic Web platform
biomedical
real-world deployment
RDFa
WebID
RDFa
WebID
Drupal as a Semantic Web platform
real-world deployment
Tutorial Organizer
2012-11-13T14:00:00+05:00
Evaluation of reasoning with ontologies
2012-11-13T15:30:00+05:00
Tommaso
Tommaso Di Noia
Di Noia
Tommaso Di Noia
521d0f3de6896cd4408128038bbe40547f790324
Technical University of Bari
Linked Data
Naimdjon Takhirov, Fabien Duchateau and Trond Aalberg
An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population
76490561
Knowledge Extraction
76490561
Machine Learning
An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population
Knowledge Extraction
This paper presents an approach to automatically extract entities and relationships from textual documents. The main goal is to populate a knowledge base that hosts this structured information about domain entities. The extracted entities and their expected relationships are verified using two evidence based techniques: classification and linking. This last process also enables the linking of our knowledge base to other sources which are part of the Linked Open Data cloud. We demonstrate the benefit of our approach through series of experiments with real-world datasets.
Linked Data
Linked Data, Knowledge Extraction, Machine Learning
Machine Learning
An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population
Hassan Saif
cfd1b87509b48cac8cacda96266298dd3dee5e0b
KMi, The Open University
Saif
Hassan
Hassan Saif
e3191d9363d7a24690b14c565b6ef48e6515625b
Josep Maria Brunetti Fernández
Josep Maria Brunetti Fernández
Universitat de Lleida
Brunetti Fernández
Josep Maria
Shashank Tyagi
f98a48be121617bbe5eef104b1caac2f6e295128
IT-BHU
Tyagi
Shashank
Shashank Tyagi
Thorsten Liebig
Thorsten Liebig
b8b5d199eaeda0a2baddde8d10ad26cad85bc635
Liebig
Derivo
Thorsten
This paper describes InSciTe Adaptive, which is a technology intelligence service developed by KISTI. InSciTe Adaptive supports not only technology analyzing and forecasting based on diverse types of information such as paper, patents, reports, ads, web resources and so on but also user adaptive and guiding service by intelligent recognition of user preference. InSciTe Adaptive includes 7 services focusing on technologies, products, organizations, and nations: (a) Technology Navigation, (b) Technology Trends, (c) Core Elementary Technology, (d) Convergence Technology, (e) Agents Levels, (f) Agents Partners, and (g) Technology Roadmap. These services were implemented by combining Semantic Web technologies with text mining technologies.
User Intention
Semantic Web,User Adaptive,Technology Trends Analysis,User Intention,Technology Prediction
Semantic Web
InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention
Technology Trends Analysis
User Intention
Semantic Web
User Adaptive
Technology Prediction
User Adaptive
InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention
Technology Prediction
Technology Trends Analysis
Jinhyung Kim, Myunggwon Hwang, Do-Heon Jeong, Sa-Kwang Song and Hanmin Jung
InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention
Machine learning has become increasingly important in the context of Linked Data as it is an enabling technology for many important tasks such as link prediction, information retrieval or group detection. The fundamental data structure of Linked Data is a graph. Graphs are also ubiquitous in many other fields of application, such as social networks, bioinformatics or the World Wide Web. Recently, tensor factorizations have emerged as a highly promising approach to machine learning on graph-structured data, showing both scalability and excellent results on benchmark data sets, while matching perfectly to the triple structure of RDF. This tutorial will provide an introduction to tensor factorizations and their applications for machine learning on graphs. By the means of concrete tasks such as link prediction we will discuss several factorization methods in-depth and also provide necessary theoretical background on tensors in general. Emphasis is put on tensor models that are of interest to Linked Data, which will include models that are able to factorize large-scale graphs with millions of entities and known facts or models that can handle the open-world assumption of Linked Data. Furthermore, we will discuss tensor models for temporal and sequential graph data, e.g. to analyze social networks over time.
Machine Learning, Tensor, Graph, Linked Data
Linked Data
Graph
Tensor
Machine Learning
2012-11-11T14:00:00+05:00
Learning on Linked Data: Tensors and their Applications in Graph-Structured Domains
Machine Learning
TAG
Graph
2012-11-11T17:30:00+05:00
Linked Data
Tensor
da50c1e462618c97bab0b228703c098fa2f61eca
Enrico Daga
Daga
Enrico Daga
KMi, The Open University;STLab, ISTC-CNR
Enrico
2
Semantic Web
Rule Bases
Rule Editor
RuleML
Thetida Zetta, Efstratios Kontopoulos and Nick Bassiliades
S2REd: A Semantic Web Rule Editor
Ontologies
Rule Editor
Semantic Web
S2REd: A Semantic Web Rule Editor
S2REd: A Semantic Web Rule Editor
A key factor in the further progress of the Semantic Web is the development and wide-spread usage of rule- and logic-based applications. However, there is an evident lack of software tools that can assist end-users in developing such applications. Consequently, users usually resort to more generic tools that offer support at a syntactic level, but prove inadequate in semantically supporting the user. This paper presents S2REd, a Semantic Web rule editor that introduces a supplementary layer of semantic assistance during rule base development. The tool offers semantic assistance via: (i) The Semantic Tag Mapping window that provides a meta-modeling facility for generating schemas over various rule language versions and, (ii) the Namespace Dialog window, for loading ontologies that serve as the underlying vocabulary for expressing rule atoms. S2REd assists in developing RuleML rule bases, but is equally suitable for any other XML-based syntax for representing rule sets.
Ontologies
RuleML
Semantic Web,RuleML,Rule Bases,Rule Editor,Ontologies
Rule Bases
Esteban Zimanyi
Université Libre de Bruxelles
Zimanyi
Esteban
Esteban Zimanyi
University of Turin
University of Turin
1
Magnus White
4fa7b334e50b4b9ed26b3e89afc72f7fdcef2734
University of Southampton
White
Magnus
Magnus White
Guus Schreiber
VU Amsterdam
Schreiber
Guus
Guus Schreiber
Griffith University
Griffith University
Raphaël
76b8645ac23d412d99c23dd95e0fbbe092d3f730
Raphaël Troncy
Raphaël Troncy
EURECOM
Troncy
Alessandro Bozzon
da1df13f99af0ce33a046cfbf5cb3f038812a620
Politecnico di Milano
Bozzon
Alessandro
Alessandro Bozzon
MODUL University Vienna
MODUL University Vienna
data aggregation
Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment
enterprise data
linked data
Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment
enterprise data
Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment
data meshing
semantic web
data meshing
linked enterprise data
semantic web
data aggregation
linked data
Fabrice Lacroix
linked data,semantic web,linked enterprise data,enterprise data,data aggregation,data meshing
linked enterprise data
Poster/Demo Session, Semantic Web Challenge and Reception
2012-11-13T18:30:00+05:00
2012-11-13T21:00:00+05:00
Arne Peters
b6bb83df9d222533f57914ce8deb43f53e7d5699
University of Koblenz and Landau
Peters
Arne
Arne Peters
Todd Minning
e963063b77dfbd04bc7955426944af0a74d8e606
University of Georgia
Minning
Todd
Todd Minning
Ivan Cantador
b16b236b808b9772d4dba554486b73a41b572ad1
Universidad Autonoma de Madrid
Cantador
Ivan
Ivan Cantador
Blazej Bulka
660886530570cd0c43286de5309ef2f90404d7ab
Clark & Parsia
Bulka
Blazej
Blazej Bulka
UNI Klagenfurt
UNI Klagenfurt
distant supervision
IE
information extraction
Freebase
Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web
web scale IE
information extraction
Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web
rule based RE
We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision learns several relations in parallel, enabling a new method of rule filtering. The system detects both binary and n-ary relations. We target 39 relations from Freebase, for which 3M sentences extracted from 20M web pages serve as the basis for learning an average of 40K distinctive rules per relation. Employing an efficient dependency parser, the average run time for each relation is only 19 hours. We compare these rules with ones learned from local corpora of different sizes and demonstrate that the Web is indeed needed for a good coverage of linguistic variation.
Freebase
relation extraction
information extraction, IE, relation extraction, RE, rule based RE, web scale IE, distant supervision, Freebase
web scale IE
Sebastian Krause, Hong Li, Hans Uszkoreit and Feiyu Xu
relation extraction
76490257
IE
RE
RE
76490257
rule based RE
Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web
distant supervision
Richard Boyce
Richard
Boyce
University of Pittstburgh
66afecdfcc01d6da44ec673d887456361bc85ef3
Richard Boyce
Christian Bizer
Bizer
Free University of Berlin
Christian
Christian Bizer
2429456d56d1f93b71e1f87c26dc3e9acc88ea49
Shonali Krishnaswamy
f6ef54138de4ab56a6b07816c9377dac695a256a
Monash University
Krishnaswamy
Shonali
Shonali Krishnaswamy
Tutorial Organizer
Leora Morgenstern
8f193fe7daa2375be876241e0ee91eadc2775510
SAIC
Morgenstern
Leora
Leora Morgenstern
Ruben Verborgh
Ghent University
fb22bc1100f1f5b282380024f58bf4e906fd3e69
Verborgh
Ruben Verborgh
Ruben
4c3085b2c9a9fa05ddd0b0148fba4994b101ad7a
Lehigh University
Dezhao Song
Dezhao
Dezhao Song
824fc6ddb4837dfb6a25ed5077be0bddce8aa377
Song
2
RDF Query Processing
RDF Query Processing
Cloud Systems
Cloud Systems
Cloud computing platforms such as Amazons EC2 and Hadoop have had significant adoption as large scale data processing platforms. Their attraction is the possibility of fault-tolerant execution and elastic scaling up or down of resources based on user requirements with minimal administrative burden to users. The rapid surge in volume of available RDF data has sharpened focus on the issue of large scale processing of RDF, making RDF query processing in the cloud an important topic. RDF data processing in the cloud requires considerations about appropriate storage models and computing environments and the impact of their underlying assumptions on RDFs schema-last and join-intensive workloads. Some example considerations are the absence of indexes and statistics usually relied on by traditional query optimization techniques and the heavy materialization in runtime execution environments such as Hadoop vs. pipelined execution plans used in traditional data processing systems. This tutorial will cover RDF query processing in the cloud, particularly MapReduce execution platforms such as Hadoop. It will overview the basics of cloud computing and cloud data storage systems, optimization issues and the state-of-the-art in RDF query optimization for cloud systems. It will also discuss future directions and open issues in query optimization for MapReduce environments.
RDF Query Processing
RDF Query Processing, Cloud Systems, MapReduce, Query Optimization, RDF Query Processing
2012-11-11T17:30:00+05:00
RDF Query Processing in the Cloud
MapReduce
Query Optimization
CLOUD
RDF Query Processing
Query Optimization
MapReduce
2012-11-11T09:00:00+05:00
linked data
entity mining
semantic search
X-ENS: Semantic Enrichment of Web Search Results at Real-Time
X-ENS: Semantic Enrichment of Web Search Results at Real-Time
Pavlos Fafalios and Yannis Tzitzikas
entity mining
web search
linked data
web search
semantic search
While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted search-like interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories.
semantic search,web search,entity mining,linked data
X-ENS: Semantic Enrichment of Web Search Results at Real-Time
Jean Christoph Jung
c7b9c84be52c7c9e0b88a07514137e3a3388206c
University of Bremen
Jung
Jean
Jean Christoph Jung
Maximilian Nickel
Nickel
Ludwig Maximilians University of Munich
Maximilian
17010f98e03b0056dabe8530aa20d93a9f3a04d5
Maximilian Nickel
SRI International
SRI International
Linked Data at The Open University: From Technical Challenges to Organizational Innovation
linked data,education,linked universities,applications
linked universities
Mathieu d'Aquin and Stuart Brown
Linked Data at The Open University: From Technical Challenges to Organizational Innovation
Linked Data at The Open University: From Technical Challenges to Organizational Innovation
linked data
applications
linked universities
linked data
applications
education
education
Thomas Malone
Malone
Thomas Malone
MIT Sloan School of Management
Thomas
Thomas W. Malone from MITs Sloan School of Management has been confirmed as the first keynote speaker for ISWC 2012. Tom heads the Center for Collective Intelligence at MIT, where he investigates how new organizations can be designed to take advantage of the possibilities provided by information technology. Toms more recent research work studies the future or work and, in particular, the transformative capabilities of collective intelligence.
7d4e8f673f0eddcb502e79be07481c507f9bc7f4
Welty
IBM Research
Chris
0e3d5ea039c6eb1e869a0fa320904d0984829e82
Chris Welty
Chris Welty
Antoniou
FORTH-ICS
f44cd7769f416e96864ac43498b082155196829e
Grigoris
Grigoris Antoniou
Grigoris Antoniou
University of Aberdeen
University of Aberdeen
Epimorphics
Epimorphics
TU Chemnitz
TU Chemnitz
Thomas Scharrenbach
University of Zurich
Scharrenbach
Thomas
Thomas Scharrenbach
Humboldt University of Berlin
Hartig
Olaf
9c09772d208636b590bf7b41d9d1976b80f6b335
Olaf Hartig
Olaf Hartig
Hamdi Aloulou
9cd3a3a229fd15b337e3b1e63d5a46c5d28f3bb5
Image & Pervasive Access Lab (IPAL), UMI CNRS
Aloulou
Hamdi
Hamdi Aloulou
Bibliographic Data
Mining Semantic Relations between Research Areas
Data Mining
Ontology Population
Research Data,Ontology Population,Bibliographic Data,Empirical Evaluation,Scholarly Ontologies, Data Mining
Bibliographic Data
76490401
Ontology Population
Francesco Osborne and Enrico Motta
Empirical Evaluation
Research Data
Empirical Evaluation
Mining Semantic Relations between Research Areas
For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.
Mining Semantic Relations between Research Areas
Scholarly Ontologies
76490401
Scholarly Ontologies
Research Data
Data Mining
Nigam Shah
Stanford University
40f599c3935f0f17928a090c90140795afcac7ec
Shah
Nigam
Nigam Shah
Chinese Academy of Sciences
Chinese Academy of Sciences
Michael Watzke
0598807bb9ce213574fc095dd76119171b30be94
Siemens AG, Corporate Technology
Watzke
Michael
Michael Watzke
Vassilis Christophides
FORTH-ICS
Christophides
Vassilis
Vassilis Christophides
Irene
Irene Celino
Celino
Politecnico di Milano
Irene Celino
a62cfa4877ae7d8b225f36c1afeeeec08a5316ae
Ludwig Maximilians University of Munich
Ludwig Maximilians University of Munich
Miriam Fernandez
Miriam Fernandez
Fernandez
KMi, The Open University
bf9aae3c7b2fc98e26382c4d558508d885a3850f
Miriam
Information Sciences Institute, University of Southern California
Ambite
José
José Luis Ambite
1e430f2ad8c3dd42da0ac00ed2c6a7c3e6fcaeb5
José Luis Ambite
Linked Open Data
Graph navigation languages
The main goal of current Web navigation languages is to retrieve set of nodes reachable from a given node. No information is provided about the fragments of the Web navigated to reach these nodes. In other words, information about their connections is lost. This paper presents an efficient algorithm to extract relevant parts of these Web fragments and shows the importance of producing subgraphs besides of sets of nodes. We discuss examples with real data using an implementation of the algorithm in the EXpRESs tool.
Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró
Subgraph extraction
Graph navigation languages,Subgraph extraction,Linked Open Data
Extracting Relevant Subgraphs from Graph Navigation
Graph navigation languages
Subgraph extraction
Linked Open Data
Extracting Relevant Subgraphs from Graph Navigation
Extracting Relevant Subgraphs from Graph Navigation
Peter Edwards
University of Aberdeen
a39911538601368843c38e64f416b47e4be0e936
Peter Edwards
Edwards
Peter
Semantic Web Challenge
Corridor-Foyer railings
Andriana Gkaniatsou
65a16288cf4bf3bae5d06938e6afbb179c70cd01
University of Edinburgh
Gkaniatsou
Andriana
Andriana Gkaniatsou
Paolo Castagna
123092f22cf1830beb449f4ac96fa1bff32ba7c8
Talis
Castagna
Paolo
Paolo Castagna
Valentina Maccatrozzo
76500382
Burst the Filter Bubble: Using Semantic Web to Enable Serendipity
Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests. However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like. To break this "personalization bubble" we introduce the notion of serendipity as a performance measure for recommendation algorithms. For this, we first identify aspects from the user perspective, which can determine level and type of serendipity desired by users. Then, we propose a user model that can facilitate such user requirements, and enables serendipitous recommendations. The use case for this work focuses on TV recommender systems, however the ultimate goal is to explore the transferability of this method to different domains. This paper covers the work done in the first eight months of research and describes the plan for the entire PhD trajectory.
76500382
Burst the Filter Bubble: Using Semantic Web to Enable Serendipity
Burst the Filter Bubble: Using Semantic Web to Enable Serendipity
Free University of Berlin
Free University of Berlin
schema matching
Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful tactic in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes the ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data translation, query answering or navigation on the web of data. Thus, matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate.
The Seventh International Workshop on Ontology Matching
Ontology Alignment Evaluation Initiative (OAEI)
ontology alignment
2012-11-11T17:30:00+05:00
OM-2012
data interlinking
ontology alignment
ontology matching
ontology matching
semantic heterogeneity
semantic heterogeneity
Ontology Alignment Evaluation Initiative (OAEI)
schema matching
data interlinking
2012-11-11T09:00:00+05:00
ontology matching, semantic heterogeneity, Ontology Alignment Evaluation Initiative (OAEI), ontology alignment, schema matching, data interlinking
ISTI-CNR
ISTI-CNR
Bastien Rance
909b0a734830ebcf7821071a6a9e60d763494408
National Library of Medicine
Rance
Bastien
Bastien Rance
Tutorial Organizer
Gerd Gröner
University of Koblenz and Landau
Gröner
Gerd
Gerd Gröner
Amal
Amal Zouaq
Amal Zouaq
c3bf2cac63d1143642d4108acb2fc9abc10c9ba6
Royal Military College of Canada
Zouaq
2012-11-13T17:30:00+05:00
2012-11-13T16:00:00+05:00
In this session, authors of a submission to the posters and demonstration track as well as the Semantic Web Challenge provide a 1 minute teaser for their poster or demo. This year, we have an exciting mix of submissions treating questions such as "How can we interact efficiently with Linked Data?", "How can we visualize data in the Semantic Web?" or "How can we query and analyze Semantic Web data?
Poster, Demo, and SWC Minute Mandess
Universidad Politecnica de Madrid
Universidad Politecnica de Madrid
Giorgos Stoilos
bccc3af684dc47522826944b0646170213c93b7e
National Technical University of Athens
Stoilos
Giorgos
Giorgos Stoilos
Elizabeth Daly
e5dfa2c5450996c2665562bf24926e9793007af1
IBM Research
Daly
Elizabeth
Elizabeth Daly
Yuki Yamagata
01410a79943f8abcd9ab91ea078bdc13e222052a
Osaka University
Yamagata
Yuki
Yuki Yamagata
Matthew Perry
Matthew
Oracle
9a80a79242529f41c8d7478d2d29dbe243392954
Matthew Perry
Perry
81bc18b08295b9b05944ffc7b9d2f2363d714f13
University of Bari
University of Bari
1
Université de Montréal
Université de Montréal
Kalyanpur
Aditya Kalyanpur
IBM Research
Aditya Kalyanpur
Aditya
b1fe362f3eabc1dac5c9829fb53fd2de0d556f5b
Sugar Labs
Sugar Labs
Halpin
c5e75a0dd882184416c8680f5c402a261314bb79
Harry Halpin
Harry Halpin
Harry
University of Edinburgh