Jérôme Euzenat, Pavel Shvaiko

Ontology Matching

Second edition

Springer-Verlag, Berlin Heidelberg (DE), 2013
xviii+511pp. (incl. bibl., index, exercises and solutions), 98 figures, 20 tables, 646 references
ACM Classification (1998): H.3, H.4, I.2, F.4

ISBN (hardcover): 978-3-642-38720-3; (eBook): 978-3-642-38721-0
DOI: 10.1007/978-3-642-38721-0; LoC#: 2013952732
Online order by Springer Verlag Approx. 99$00 or 69€99.

Presentation

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 150 than 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.

Table of contents

Click on sections to expand or hide / show all

Preface
Part I The matching problem
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
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
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
Part II Ontology matching techniques
4 Classifications 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 Classification of matching approaches
4.2.1 Methodology
4.2.2 Granularity/Input interpretation
4.2.3 Origin/Kind of input
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 classifications
4.5 Summary
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 identification 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
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
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
Summary on alignment improvement
7.9 Summary
Part III Systems and evaluation
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 (Università di Reggio Calabria and Università di Calabria)
8.1.5 SKAT and ONION (Stanford University)
8.1.6 Artemis (Università di Milano and Università di Modena e Reggio Emilia)
8.1.7 H-Match (Università degli Studi di Milano)
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, Universität 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 Telecomunication 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)
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 (Università di 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, Griffith 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 M¨nchen 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)
8.2.12 sPLMap (University of Duisburg-Essen, and ISTI-CNR)
8.2.13 FSM (Poland National Institute of Telecommunications, Humboldt-Universität zu Berlin, Max Plank Institute for ComputerScience)
8.2.14 VSBM & GBM (École Centrale Paris)
8.2.15 ProbaMap (Université de Grenoble)
8.3 Mixed, schema-based and instance-based systems
8.3.1 SEMINT (Northwestern University, NEC and The MITRE Corporation)
8.3.2 IF-Map (University of Southampton and University of Edinburgh)
8.3.3 NOM and QOM (University of Karlsruhe)
8.3.4 oMap (CNR Pisa)
8.3.5 Xu and Embley (Brigham Young University)
8.3.6 Wise-Integrator (SUNY at Binghamton, University of Illinois at Chicago and University of Louisiana at Lafayette)
8.3.7 IceQ (University of Illinois at Urbana-Champaign, University of Illinois at Chicago, SUNY at Binghamton)
8.3.8 OLA (INRIA Rhône-Alpes and Université de Montréal)
8.3.9 Falcon-AO (China Southeast University)
8.3.10 RiMOM (Tsinghua University)
8.3.11 Corpus-based matching (University of Washington, Microsoft Research and University of Illinois)
8.3.12 iMapper (Norwegian University of Science and Technology)
8.3.13 SAMBO (Linköpings University)
8.3.14 AROMA (University of Nantes, INRIA)
8.3.15 ILIADS (University of Maryland, University of Toronto)
8.3.16 SeMap (Georgia Tech, University of British Columbia)
8.3.17 ASMOV (INFOTECH Soft, Inc., University of Miami)
8.3.18 HAMSTER (University of Michigan, Microsoft Research)
8.3.19 Smart Matcher (Vienna University of Technology)
8.3.20 GEM/Optima/Optima+ (University of Georgia, Wright State University)
8.3.21 CSR (University of the Aegean, Institution of Informatics and Telecommunications)
8.3.22 Prior+ (SAP Labs, Yahoo!, University of Pittsburgh)
8.3.23 YAM & YAM++ (University of Montpellier, University of Toronto)
8.3.24 MoTo (University of Bari)
8.3.25 CODI (Universität Mannheim)
8.3.26 LogMap (University of Oxford)
8.3.27 PARIS (INRIA, Télécom ParisTech)
8.4 Metamatching systems
8.4.1 APFEL (University of Karlsruhe and University of Koblenz-Landau)
8.4.2 LCS (Queen's University Belfast)
8.4.3 Besana and Robertson (University of Edinburgh)
8.4.4 eTuner (University of Illinois and The MITRE Corporation)
8.4.5 mSeer (University of Wisconsin-Madison, The MITRE Corporation)
8.4.6 GOALS (Gecad -- Polytechnic of Porto)
8.4.7 ContentMap (Universitat Jaume I, University of Oxford)
8.4.8 SMB (Technion Israel Institute of Technology)
8.4.9 AMC (SAP Research, University of Leipzig)
8.4.10 AMS (SAP Research, Dresden University of Technology, University of Leipzig)
8.5 Summary
9 Evaluation of matching systems
9.1 Evaluation principles
9.1.1 Goals
9.1.2 Principles
9.1.3 Examples of evaluations
Text REtrieval Conference
Ontology Alignment Evaluation Initiative
9.1.4 Types of evaluations
9.1.5 Automation
9.2 Data sets for evaluation
9.2.1 Dimensions and variability of alignment evaluation
Input ontologies
Input alignment
Parameters and resources
Output alignment
Matching process
9.2.2 Examples of data sets
OAEI systematic benchmark suite
Large scale ontology sets
Directory sets
Thesauri
Other test collections
9.2.3 Test generation
9.3 Evaluation measures
9.3.1 Compliance measures
9.3.2 Generalising precision and recall
Weighted precision and recall
Relaxed precision and recall
Semantic precision and recall
9.3.3 Sampling and relative precision and recall
9.3.4 Performance measures
Speed
Network
Memory
Scalability
9.3.5 User-related measures
Level of user input effort
Oracle-based measures
General subjective satisfaction
9.4 Application-specific evaluation
9.4.1 Aggregating measures
9.4.2 Evaluation setting
9.5 Summary
Part IV Representing, explaining, and processing alignments
10 Frameworks and formats: representing alignments
10.1 Alignment formats
10.1.1 MAFRA Semantic bridge ontology (SBO)
10.1.2 OWL
10.1.3 Contextualized OWL (C-OWL)
10.1.4 SWRL and RIF
10.1.5 Alignment format
10.1.6 Expressive and declarative ontology alignment language (EDOAL)
10.1.7 SKOS
Concept and relation descriptions
Mapping vocabulary
10.1.8 Comparison of existing formats
10.2 Alignment metadata
10.2.1 Identification metadata
10.2.2 Provenance metadata
10.2.3 Qualification metadata
10.3 Alignment frameworks
10.3.1 Model management
10.3.2 COMA++ (University of Leipzig)
10.3.3 GOMMA (University of Leipzig)
10.3.4 MAFRA (Instituto Politecnico do Porto and University of Karlsruhe)
10.3.5 The Protégé Prompt Suite (Stanford University)
10.3.6 Alignment API and implementation (INRIA)
Classes
Functions
10.3.7 FOAM (University of Karlsruhe)
10.3.8 Harmony (MITRE)
10.3.9 The NeOn Toolkit alignment plug-in
10.4 Summary
11 User involvement
11.1 Individual matching
11.1.1 Providing input
11.1.2 Manual matcher composition
11.1.3 Relevance feedback
11.2 Collective matching
11.2.1 Community-driven ontology matching
11.2.2 Crowdsourcing ontology matching
11.3 Explaining alignments
11.3.1 Explanation approaches
The proof presentation approach
The strategic flow approach
The argumentation approach
11.3.2 A default explanation
The S-Match example
The iMAP example
An argumentation example
11.3.3 Explaining basic matchers
11.3.4 Explaining the matching process
Dependency graphs
Explaining logical reasoning
11.4 Alignment editors and visualisers
11.4.1 WSMT (DERI, University of Innsbruck)
11.4.2 Muse (University of California, University of Toronto)
11.4.3 iMerge (Duisbourg U.)
11.4.4 Chimaera (Stanford University)
11.4.5 iPrompt (Stanford University)
11.4.6 AlViz (Vienna University of Technology, Norwegian University of Science and Technology)
11.4.7 CogZ (University of Victoria)
11.5 Summary
12 Processing alignments
12.1 Ontology merging
OntoMerge (Yale University and University of Oregon)
12.2 Ontology transformation
12.3 Data translation
Clio (IBM Almaden and University of Toronto)
Spicy (Università della Basilicata, ICAR-CNR)
12.4 Data interlinking
KnoFuss (The Open University)
Silk (Chemnitz University of Technology, Freie Universität Berlin)
12.5 Mediation
12.6 Reasoning
12.7 Alignment services and repositories
BioPortal (Stanford University)
Alignment server (INRIA)
CATCH (Vrije Universiteit)
12.8 Alignment evolution
ToMAS (University of Toronto and IBM Almaden)
12.9 Summary
Part V Conclusions
13 Conclusions
13.1 A brief outlook of the trends in the field
13.2 Future challenges
13.2.1 Large-scale and efficient matching
13.2.2 Matching with background knowledge
13.2.3 Matcher selection, combination and tuning
13.2.4 User involvement
13.2.5 Social and collaborative matching
13.2.6 Uncertainty in ontology matching
13.2.7 Reasoning with alignments
13.2.8 Alignment management: infrastructure and support
13.3 Final words
Appendices
Appendix A: Legends of figures
Appendix B: Running example
Appendix C: Exercises
Appendix D: Solution to exercises
References
Index

Index

A fully searchable index of the book is available. It covers more terms and more references than the index published with the book.

Glossary

The terminology as used in this book is made available.

Errata

The current errata is available.

Please do not hesitate to make us aware of problems you find with the book.

First edition

The web site for the first edition is available here.

Bibliography

The bibtex files used for the book is split into the updated 1st edition bibtex file and the suplementary 2nd edition bibtex file.

Other references can be found in the ontologymatching.org site.

BibTeX entry

@book{euzenat2013d,
        author          =       {J\'er\^ome Euzenat and Pavel Shvaiko},
        title           =       {Ontology matching},
        edition         =       {2nd},
        language        =       {english},
        page            =       520,
        publisher       =       {Springer-Verlag},
        address         =       {Heidelberg (DE)},
        year            =       2013}
or for biber users: 
@book{euzenat2013d,
        author          =       {J\'er\^ome Euzenat and Pavel Shvaiko},
        title           =       {Ontology matching},
        edition         =       {2nd},
        language        =       {english},
        page            =       520,
        publisher       =       {Springer-Verlag},
        address         =       {Heidelberg (DE)},
        year            =       2013,
        url             =       {http://book.ontologymatching.org},
        isbn            =       {978-3-642-38720-3}}

 

http://book.ontologymatching.org