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
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Presentation
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
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Part I The matching problem
1.1.1 Ontology editing and import
1.1.2 Ontology evolution and versioning
1.2.1 Schema integration
1.2.2 Catalogue integration
1.2.3 Data integration
1.3.1 Semantic P2P systems
1.3.2 Emergent semantics between peers
1.4 Web service composition
1.5.1 Multi-agent communication
1.5.2 Matching contexts in ambient computing
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
2.1.1 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
2.2.1 Ontology entities
2.2.2 Ontology language semantics
2.3 Types of heterogeneity
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
3.1.1 Input dimensions
3.1.2 Process dimensions
3.1.3 Output dimensions
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
4.1 Similarity, distances and other measures
Normalisation
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
Property comparison and keys
Datatype comparison
Domain comparison
Comparing multiplicities and properties
Other features
Summary on internal structure
Taxonomic structure
Mereologic structure
Relations
Summary on relational structure
4.4.2 Instance identification techniques
Statistical approach
Similarity-based extension comparison
Matching-based comparison
Summary on extensional techniques
4.5.1 Techniques based on external ontologies
Propositional techniques
Description logic techniques
Summary on semantic techniques
4.6 Summary
5.1 Matcher composition
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
5.3.1 Similarity flooding
5.3.2 Similarity equation fixed point
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
5.6.1 Providing input
5.6.2 Manual matcher composition
5.6.3 Relevance feedback
5.7.1 Thresholds
5.7.2 Strengthening and weakening
5.7.3 Optimising the result
5.8 Summary
Part III Systems and evaluation
6.1.1 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)
6.2.1 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)
6.3.1 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)
6.4.1 APFEL (University of Karlsruhe and University of Koblenz-Landau)
6.4.2 eTuner (University of Illinois and The MITRE Corporation)
6.5 Summary
7.1.1 Goals
7.1.2 Principles
Text REtrieval Conference
Ontology Alignment Evaluation Initiative
7.1.4 Types of evaluations
Input ontologies
Input alignment
Parameters and resources
Output alignment
Matching process
OAEI systematic benchmark suite
Large scale ontology sets
Directory sets
Thesauri
Other test collections
Non equal correspondences
7.3.2 Generalising precision and recall
Level of user input effort
General subjective satisfaction
7.4.1 Aggregating measures
7.4.2 Evaluation setting
7.5 Summary
Part IV Representing, explaining, and processing alignments
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
Annotations
8.1.8 Comparison of existing formats
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)
8.3.1 Chimaera (Stanford University)
8.3.2 The Protégé Prompt Suite (Stanford University)
8.4 Summary
9.1.1 Information about basic matchers
9.1.2 Process traces
9.2.1 The proof presentation approach
9.2.2 The strategic flow approach
9.2.3 The argumentation approach
9.3.1 The S-Match example
9.3.2 The iMAP example
9.4 Explaining basic matchers
9.5.1 Dependency graphs
9.5.2 Explaining logical reasoning
9.6 Arguing about correspondences
9.7 Summary
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
11.1 A brief outlook of the trends in the field
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
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.
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.
Errata
The current errata is available.
Please do not hesitate to make us aware of problems you find with
the book.
Bibliography
The bibtex file used for the book is
available here.
Other references can be found in
the ontologymatching.org site.
BibTeX entry
@book{euzenat2007b,
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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