Jérôme Euzenat, Pavel Shvaiko

Ontology Matching

Springer-Verlag, Berlin Heidelberg (DE), 2007
X+333pp. (incl. bibl., index and exercises), 67 figures, 18 tables, hardcover
ACM Classification (1998): H.3, H.4, I.2, F.4

ISBN (hardcover): 978-3-540-49611-3; LoC#: 2007926257
ISBN (paperback): 978-3-642-08055-5
Online order by Springer Verlag Approx. 79$95 or 63€25 or 46£.


Ontologies tend to be 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 finds 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.

With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the presented work and techniques can equally be applied to database schema matching, catalog integration, XML schema matching and other related problems. The book presents the state of the art and the latest research results in ontology matching by providing a detailed account of matching techniques and matching systems in a systematic way from theoretical, practical and application perspectives.

Table of contents

Click on sections to expand or hide / show all

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 Peer-to-peer information sharing
1.3.1 Semantic P2P systems
1.3.2 Emergent semantics between peers
1.4 Web service composition
1.5 Autonomous communication systems
1.5.1 Multi-agent communication
1.5.2 Matching contexts in ambient computing
1.6 Navigation and query answering on the web
1.6.1 Navigation on the semantics web
1.6.2 Query answering on the web
1.6.3 Query answering on the deep web
1.7 Summary
2 The matching problem
2.1 Vocabularies, schemas and ontologies
2.1.1 Tags and folkosomies
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 A rough understanding of matching
2.5.4 Semantics of alignment
2.6 Summary
Part II Ontology matching techniques
3 Classifications of ontology matching techniques
3.1 Matching dimensions
3.1.1 Input dimensions
3.1.2 Process dimensions
3.1.3 Output dimensions
3.2 Classification of matching approaches
String-based techniques
Language-based techniques
Constraint-based techniques
Linguistic resources
Alignment reuse
Upper level and domain specific formal ontologies
Graph-based techniques
Taxonomy-based techniques
Repository of structures
Model-based techniques
Data analysis and statistics techniques
3.3 Other classifications
3.4 Summary
4 Basic techniques
4.1 Similarity, distances and other measures
4.2 Name-based techniques
String equality
Substring test
Edit distance
Token-based distances
Path comparison
Summary on string-based methods
Intrinsic methods: Linguistic normalisation
Extrinsic methods
Summary on linguistic methods
4.3 Structure-based techniques
Property comparison and keys
Datatype comparison
Domain comparison
Comparing multiplicities and properties
Other features
Summary on internal structure
Taxonomic structure
Mereologic structure
Summary on relational structure
4.4 Extensional techniques
Formal concept analysis
4.4.2 Instance identification techniques
Statistical approach
Similarity-based extension comparison
Matching-based comparison
Summary on extensional techniques
4.5 Semantic-based techniques
4.5.1 Techniques based on external ontologies
Propositional techniques
Description logic techniques
Summary on semantic techniques
4.6 Summary
5 Matching strategies
5.1 Matcher composition
5.2 Similarity aggregation
5.2.1 Triangular norms
5.2.2 Multidimentional distances and weighted sums
5.2.3 Fuzzy aggregation and weighted average
5.2.4 Ordered weighted average
5.3 Global similarity computation
5.3.1 Similarity flooding
5.3.2 Similarity equation fixed point
5.4 Learning methods
5.4.1 Bayes learning
5.4.2 WHIRL learner
5.4.3 Neural networks
5.4.4 Decision trees
5.4.5 Stacked generalisation
5.5 Probabilistic methods
5.5.1 Bayesian networks
5.6 User involvement and dynamic composition
5.6.1 Providing input
5.6.2 Manual matcher composition
5.6.3 Relevance feedback
5.7 Alignment extraction
5.7.1 Thresholds
5.7.2 Strengthening and weakening
5.7.3 Optimising the result
5.8 Summary
Part III Systems and evaluation
6 Overview of matching systems
6.1 Schema-based systems
6.1.1 DELTA (The MITRE Corporation)
6.1.2 Hovy (University of Southern California)
6.1.3 TransScm (Tel Aviv University)
6.1.4 DIKE (Università di Reggio Calabria and Università di Calabria)
6.1.5 SKAT and ONION (Stanford University)
6.1.6 Artemis (Università di Milano and Università di Modena e Reggio Emilia)
6.1.7 H-Match (Università degli Studi di Milano)
6.1.8 Tess (University of Massachusetts)
6.1.9 Anchor-Prompt (Stanford Medical Informatics)
6.1.10 OntoBuilder (Technion Israel Institute of Technology)
6.1.11 Cupid (University of Washington, Microsoft Corporation and University of Leipzig)
6.1.12 COMA and COMA++ (University of Leipzig)
6.1.13 Similarity flooding (Stanford University and University of Leipzig)
6.1.14 XClust (National University of Singapore)
6.1.15 ToMAS (University of Toronto and IBM Almaden)
6.1.16 MapOnto (University of Toronto and Rutgers University)
6.1.17 OntoMerge (Yale University and University of Oregon)
6.1.18 CtxMatch and CtxMatch2 (University of Trento and ITC-IRST)
6.1.19 S-Match (University of Trento)
6.1.20 HCONE-merge (University of the Aegean)
6.1.21 MoA (Electronics and Telecomunication Research Institute, ETRI)
6.1.22 ASCO (INRIA Sophia-Antipolis)
6.1.23 BayesOWL and BN mapping (University of Maryland)
6.1.24 OMEN (The Pennsylvania State University and Stanford University)
6.1.25 DCM framework (University of Illinois at Urbana-Champaign)
6.2 Instance-based systems
6.2.1 T-tree (INRIA Rhône-Alpes)
6.2.2 CAIMAN (Technische Universität München and Universität Kaiserslautern)
6.2.3 FCA-merge (University of Karlsruhe)
6.2.4 LSD (University of Washington)
6.2.5 GLUE (University of Washington)
6.2.6 iMAP (University of Illinois and University of Washington)
6.2.7 Automatch (George Mason University)
6.2.8 SBI&NB (The Graduate University for Advanced Studies)
6.2.9 Kang and Naughton (University of Wisconsin-Madison)
6.2.10 Dumas (Technische Universität Berlin and Humboldt-Universität zu Berlin)
6.2.11 Wang and colleagues (Hong Kong University of Science and Technology and Microsoft Research Asia)
6.2.12 sPLMap (University of Duisburg-Essen, and ISTI-CNR)
6.3 Mixed, schema-based and instance-based systems
6.3.1 SEMINT (Northwestern University, NEC and The MITRE Corporation)
6.3.2 Clio (IBM Almaden and University of Toronto)
6.3.3 IF-Map (University of Southampton and University of Edinburgh)
6.3.4 NOM and QOM (University of Karlsruhe)
6.3.5 oMap (CNR Pisa)
6.3.6 Xu and Embley (Brigham Young University)
6.3.7 Wise-Integrator (SUNY at Binghamton, University of Illinois at Chicago and University of Louisiana at Lafayette)
6.3.8 OLA (INRIA Rhône-Alpes and Université de Montréal)
6.3.9 Falcon-AO (China Southwest University)
6.3.10 RiMOM (Tsinghua University)
6.3.11 Corpus-based matching (University of Washington, Microsoft Research and University of Illinois)
6.4 Meta-matching systems
6.4.1 APFEL (University of Karlsruhe and University of Koblenz-Landau)
6.4.2 eTuner (University of Illinois and The MITRE Corporation)
6.5 Summary
7 Evaluation of matching systems
7.1 Evaluation principles
7.1.1 Goals
7.1.2 Principles
Text REtrieval Conference
Ontology Alignment Evaluation Initiative
7.1.4 Types of evaluations
7.2 Data sets for evaluation
Input ontologies
Input alignment
Parameters and resources
Output alignment
Matching process
OAEI systematic benchmark suite
Large scale ontology sets
Directory sets
Other test collections
7.3 Evaluation measures
Non equal correspondences
7.3.2 Generalising precision and recall
Level of user input effort
General subjective satisfaction
7.4 Application-specific evaluation
7.4.1 Aggregating measures
7.4.2 Evaluation setting
7.5 Summary
Part IV Representing, explaining, and processing alignments
8 Frameworks and formats: representing alignments
8.1 Alignment formats
8.1.1 MAFRA Semantic bridge ontology (SBO)
8.1.2 OWL
8.1.3 Contextualized OWL (C-OWL)
8.1.4 SWRL
8.1.5 Alignment format
8.1.6 SEKT mapping language
Concept and relation descriptions
Concept relations
8.1.8 Comparison of existing formats
8.2 Alignment frameworks
8.2.1 Model management
8.2.2 COMA++ (University of Leipzig)
8.2.3 MAFRA (Instituto Politecnico do Porto and University of Karlsruhe)
8.2.5 FOAM (University of Karlsruhe)
8.3 Ontology editors with alignment manipulation capabilities
8.3.1 Chimaera (Stanford University)
8.3.2 The Protégé Prompt Suite (Stanford University)
8.4 Summary
9 Explaining alignments
9.1 Justifications
9.1.1 Information about basic matchers
9.1.2 Process traces
9.2 Explanation approaches
9.2.1 The proof presentation approach
9.2.2 The strategic flow approach
9.2.3 The argumentation approach
9.3 A default explanation
9.3.1 The S-Match example
9.3.2 The iMAP example
9.4 Explaining basic matchers
9.5 Explaining the matching process
9.5.1 Dependency graphs
9.5.2 Explaining logical reasoning
9.6 Arguing about correspondences
9.7 Summary
10 Processing alignments
10.1 Ontology merging
10.2 Ontology transformation
10.3 Data translation
10.4 Mediation
10.5 Reasoning
10.6 Towards an alignment service
10.7 Summary
Part V Conclusions
11 Conclusions
11.1 A brief outlook of the trends in the field
11.2 Future challenges
11.2.1 Applications
11.2.2 Foundations
11.2.3 Basic techniques
11.2.4 Matching strategies
11.2.5 Matching systems
11.2.6 Evaluation of matching systems
11.2.7 Representing alignments
11.2.8 Explaining alignments
11.2.9 Processing alignments
11.3 Final words
Appendix A: Legends of figures
Appendix B: Running example
Appendix C: Exercises


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


The terminology as used in this book is made available.

Exercises and solutions

Finally, the solutions to exercises are available as an additional book appendix (PDF) as well as a directory of ontology and alignments with processing instructions for generating solutions with the Alignment API.


The current errata is available.

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


The bibtex file used for the book is available here.

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

BibTeX entry

        author          =       {J\'er\^ome Euzenat and Pavel Shvaiko},
        title           =       {Ontology matching},
        language        =       {english},
        page            =       341,
        publisher       =       {Springer-Verlag},
        address         =       {Heidelberg (DE)},
        year            =       2007,
        isbn            =       {3-540-49611-4}}


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