Ontology matching
This book explores ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Also covers emerging topics such as data interlinking, ontology partitioning and pruning, and user involvement in matching.
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Berlin, Heidelberg
Springer
2013
Springer Berlin / Heidelberg Springer Berlin Heidelberg Springer-Verlag |
| Edition | 2 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3642387209 9783642387203 |
| DOI | 10.1007/978-3-642-38721-0 |
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| Abstract | This book explores ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Also covers emerging topics such as data interlinking, ontology partitioning and pruning, and user involvement in matching. |
|---|---|
| AbstractList | Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level.Euzenat and Shvaiko's book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence.The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed.With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives. This book explores ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Also covers emerging topics such as data interlinking, ontology partitioning and pruning, and user involvement in matching. |
| Author | Shvaiko, Pavel Euzenat, Jérôme |
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| Copyright | Springer-Verlag Berlin Heidelberg 2013 Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1007/978-3-642-38721-0 |
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| Edition | 2 2nd ed. 2013 Second edition. |
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| Notes | Includes bibliographical references (p. 463-495) and index |
| OCLC | 864999776 |
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| PageCount | 512 |
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| PublicationPlace | Berlin, Heidelberg |
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| Snippet | This book explores ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Also covers emerging topics such as data... Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems,... |
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| SubjectTerms | Artificial Intelligence Computer Science e-Commerce/e-business Information Storage and Retrieval Information Systems Applications (incl. Internet) IT in Business Management information systems Mathematical Logic and Formal Languages Ontologies (Information retrieval) Semantic integration (Computer systems) Web |
| TableOfContents | 8.1.29 CBW (Sharif University of Technology, Tehran Institute for Studies in Theoretical Physics and Mathematics) -- 8.1.30 GeRoMeSuite (RWTH Aachen University) -- 8.1.31 AOAS (US National Library of Medicine) -- 8.1.32 Scarlet (The Open University) -- 8.1.33 OMviaUO (University of Genova, Universidad Politécnica de Valencia) -- 8.1.34 BLOOMS/BLOOMS+ (Wright State University, Accenture Technology Labs and Ontotext AD) -- 8.1.35 CIDER (Universidad Politécnica de Madrid, University of Zaragoza) -- 8.1.36 Elmeleegy and Colleagues (Purdue University) -- 8.1.37 BeMatch (Versailles Saint-Quentin en Yvelines, University of Cauca) -- 8.1.38 PORSCHE (University of Montpellier, ETH Zurich) -- 8.1.39 MatchPlanner (University of Montpellier) -- 8.1.40 Anchor-Flood (Toyohashi University of Technology) -- 8.1.41 Lily (Southeast University, Nanjing University) -- 8.1.42 AgreementMaker (University of Illinois at Chicago) -- 8.1.43 Homolonto (University of Lausanne, Swiss Institute of Bioinformatics) -- 8.1.44 DSSim (Open University, Poznan University of Economics) -- 8.1.45 MapPSO (FZI Research Center for Information Technology, Grif th University) -- 8.1.46 TaxoMap (University of Paris-Sud 11, INRIA) -- 8.1.47 iMatch (Ben-Gurion University) -- 8.2 Instance-Based Systems -- 8.2.1 T-tree (INRIA Rhône-Alpes) -- 8.2.2 CAIMAN (Technische Universität München and Universität Kaiserslautern) -- 8.2.3 FCA-Merge (University of Karlsruhe) -- 8.2.4 LSD (University of Washington) -- 8.2.5 GLUE (University of Washington) -- 8.2.6 iMAP (University of Illinois and University of Washington) -- 8.2.7 Automatch (George Mason University) -- 8.2.8 SBI& -- NB (The Graduate University for Advanced Studies) -- 8.2.9 Kang and Naughton (University of Wisconsin-Madison) -- 8.2.10 Dumas (Technische Universität Berlin and Humboldt-Universität zu Berlin) 8.2.11 Wang and Colleagues (Hong Kong University of Science and Technology and Microsoft Research Asia) Part II: Ontology Matching Techniques -- Chapter 4: Classi cations of Ontology Matching Techniques -- 4.1 Matching Dimensions -- 4.1.1 Input Dimensions -- 4.1.2 Process Dimensions -- 4.1.3 Output Dimensions -- 4.2 Classi cation of Matching Approaches -- 4.2.1 Methodology -- 4.2.2 Granularity/Input Interpretation Layer -- 4.2.3 Origin/Kind of Input Layer -- 4.3 Classes of Concrete Techniques -- 4.3.1 Element-Level Techniques -- String-Based Techniques -- Language-Based Techniques -- Constraint-Based Techniques -- Informal Resource-Based Techniques -- Formal Resource-Based Techniques -- 4.3.2 Structure-Level Techniques -- Graph-Based Techniques -- Taxonomy-Based Techniques -- Model-Based Techniques -- Instance-Based Techniques -- 4.4 Other Classi cations -- 4.5 Summary -- Chapter 5: Basic Similarity Measures -- 5.1 Similarity, Distances and Other Measures -- 5.2 Name-Based Techniques -- 5.2.1 String-Based Methods -- Normalisation -- String Equality -- Substring Test -- Edit Distance -- Token-Based Distances -- Path Comparison -- Summary on String-Based Methods -- 5.2.2 Language-Based Methods -- Intrinsic Methods: Linguistic Normalisation -- Extrinsic Methods -- Multilingual Methods -- Summary on Linguistic Methods -- 5.3 Internal Structure-Based Techniques -- 5.3.1 Property Comparison and Keys -- 5.3.2 Data Type Comparison -- 5.3.3 Domain Comparison -- 5.3.4 Comparing Multiplicities and Properties -- 5.3.5 Other Features -- Summary on Internal Structure-Based Techniques -- 5.4 Extensional Techniques -- 5.4.1 Common Extension Comparison -- Formal Concept Analysis -- 5.4.2 Instance Identi cation Techniques -- Linkkey Extraction -- Similarity-Based Instance Matching -- 5.4.3 Disjoint Extension Comparison -- Statistical Approach -- Similarity-Based Extension Comparison -- Matching-Based Comparison -- Summary on Extensional Techniques -- 5.5 Summary Summary on Alignment Improvement -- 7.9 Summary -- Part III: Systems and Evaluation -- Chapter 8: Overview of Matching Systems -- 8.1 Schema-Based Systems -- 8.1.1 DELTA (The MITRE Corporation) -- 8.1.2 Hovy (University of Southern California) -- 8.1.3 TransScm (Tel Aviv University) -- 8.1.4 DIKE (University of Reggio Calabria and University of Calabria) -- 8.1.5 SKAT and ONION (Stanford University) -- 8.1.6 Artemis (University of Milan and University of Modena e Reggio Emilia) -- 8.1.7 H-Match (University of Milan) -- 8.1.8 Tess (University of Massachusetts) -- 8.1.9 Anchor-Prompt (Stanford Medical Informatics) -- 8.1.10 OntoBuilder (Technion Israel Institute of Technology) -- 8.1.11 Cupid (University of Washington, Microsoft Corporation and University of Leipzig) -- 8.1.12 COMA and COMA++ (University of Leipzig) -- 8.1.13 QuickMig (SAP, University of Leipzig) -- 8.1.14 Similarity Flooding (Stanford University and University of Leipzig) -- 8.1.15 XClust (National University of Singapore) -- 8.1.16 MapOnto (University of Toronto and Rutgers University) -- 8.1.17 CtxMatch and CtxMatch2 (University of Trento and ITC-IRST) -- 8.1.18 S-Match (University of Trento) -- 8.1.19 HCONE (University of the Aegean) -- 8.1.20 MoA (Electronics and Telecommunication Research Institute, ETRI) -- 8.1.21 ASCO (INRIA Sophia-Antipolis) -- 8.1.22 Stroulia & -- Wang (University of Alberta) -- 8.1.23 MWSDI (University of Georgia) -- 8.1.24 SeqDisc (University of Leipzig, Queensland University of Technology, University of Magdeburg) -- 8.1.25 BayesOWL and BN Mapping (University of Maryland) -- 8.1.26 OMEN (The Pennsylvania State University and Stanford University) -- 8.1.27 DCM Framework (University of Illinois at Urbana-Champaign) -- 8.1.28 HSM (Hong Kong University of Science and Technology, City University of Hong Kong) Chapter 6: Global Matching Methods -- 6.1 Relational Techniques -- 6.1.1 Taxonomic Structure -- 6.1.2 Mereologic Structure -- 6.1.3 Relations -- 6.1.4 Pattern-Based Matching -- Summary on Relational Techniques -- 6.2 Iterative Similarity Computation -- 6.2.1 Similarity Flooding -- 6.2.2 Similarity Equation Fixed Point -- Summary on Global Similarity Computation -- 6.3 Matching as Optimisation -- 6.3.1 Expectation Maximisation -- 6.3.2 Particle Swarm Optimisation -- Summary on Optimisation Techniques -- 6.4 Probabilistic Matching -- 6.4.1 Bayesian Networks -- 6.4.2 Markov Networks and Markov Logic Networks -- Summary on Probabilistic Matching -- 6.5 Semantic Techniques -- 6.5.1 Propositional Techniques -- 6.5.2 Description Logic Techniques -- Summary on Semantic Techniques -- 6.6 Summary -- Chapter 7: Matching Strategies -- 7.1 Ontology Partitioning and Search-Space Pruning -- 7.1.1 Partitioning -- 7.1.2 Search-Space Pruning -- 7.2 Matcher Composition -- 7.3 Context-Based Matching -- 7.4 Similarity and Alignment Aggregation -- 7.4.1 Weighting -- Triangular Norms -- Multidimensional Distances and Weighted Sums -- Fuzzy Aggregation and Weighted Average -- Harmonic Adaptive Weighted Sum -- Ordered Weighted Average -- 7.4.2 Voting -- Dempster-Shafer Theory -- 7.4.3 Arguing -- Summary on Similarity and Alignment Aggregation -- 7.5 Matching Learning -- 7.5.1 Bayes Learning -- 7.5.2 WHIRL Learner -- 7.5.3 Neural Networks -- 7.5.4 Support Vector Machines -- 7.5.5 Decision Trees -- Summary on Matcher Learning -- 7.6 Matcher Tuning -- 7.6.1 Stacked Generalisation -- 7.6.2 Genetic Algorithms -- Summary on Matcher Tuning -- 7.7 Alignment Extraction -- 7.7.1 Thresholds -- 7.7.2 Strengthening and Weakening -- 7.7.3 Optimising the Result -- Summary on Alignment Extraction -- 7.8 Alignment Improvement -- 7.8.1 Alignment Disambiguation -- 7.8.2 Alignment Debugging Intro -- Ontology Matching -- Preface -- About Ontology Matching -- Novelty of the Second Edition -- Outline of the Book -- Readership and Lecture Guide -- Acknowledgements -- Contents -- Part I: The Matching Problem -- Chapter 1: Applications -- 1.1 Ontology Engineering -- 1.1.1 Ontology Editing and Import -- 1.1.2 Ontology Evolution and Versioning -- 1.2 Information Integration -- 1.2.1 Schema Integration -- 1.2.2 Catalogue Integration -- 1.2.3 Data Integration -- 1.3 Linked Data -- 1.4 Peer-to-Peer Information Sharing -- 1.4.1 Semantic P2P Systems -- 1.4.2 Emergent Semantics Between Peers -- 1.5 Web Service Composition -- 1.6 Autonomous Communication Systems -- 1.6.1 Multiagent Communication -- 1.6.2 Matching Contexts in Ambient Computing -- 1.7 Navigation and Query Answering on the Web -- 1.7.1 Navigation on the Semantics Web -- 1.7.2 Query Answering on the Web -- 1.7.3 Query Answering on the Deep Web -- 1.8 Summary -- Chapter 2: The Matching Problem -- 2.1 Vocabularies, Schemas and Ontologies -- 2.1.1 Tags and Folksonomies -- 2.1.2 Directories -- 2.1.3 Relational Database Schemas -- 2.1.4 XML Schemas -- 2.1.5 Conceptual Models -- 2.1.6 Ontologies -- 2.2 Ontology Language -- 2.2.1 Ontology Entities -- 2.2.2 Ontology Language Semantics -- 2.3 Types of Heterogeneity -- 2.4 Terminology -- 2.5 The Ontology Matching Problem -- 2.5.1 The Matching Process -- 2.5.2 Structure of an Alignment -- 2.5.3 Towards a Semantics for Matching and Alignments -- 2.6 Summary -- Chapter 3: Methodology -- 3.1 The Alignment Life Cycle -- 3.2 Identifying Ontologies and Characterising Needs -- 3.3 Retrieving Existing Alignments -- 3.4 Selecting and Composing a Matcher -- 3.5 Matching Ontologies -- 3.6 Evaluating Alignments -- 3.7 Enhancing Alignments -- 3.8 Storing and Sharing -- 3.9 Rendering and Processing Alignments -- 3.10 Summary |
| Title | Ontology matching |
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