Ontology Matching

Cover
Springer Science & Business Media, 15.06.2007 - 333 Seiten

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, artificial intelligence.

With Ontology Matching, researchers and practitioners will find a reference book which presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can equally be 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 detailed account of matching techniques and matching systems in a systematic way from theoretical, practical and application perspectives.

 

Inhalt

Introduction
1
The matching problem
29
3
61
4
73
Matching strategies
117
6
152
Evaluation of matching systems
193
8
219
Explaining alignments
245
Processing alignments
259
Conclusions
269
Legends of figures
275
Exercises
289
References
297
Index 323
322
Urheberrecht

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Beliebte Passagen

Seite 311 - C. Clifton. SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks.
Seite 298 - Jon Barwise and Jerry Seligman. Information flow: the logic of distributed systems, volume 44 of Cambridge tracts in theoretical computer science. Cambridge University Press, Cambridge (UK), 1997.
Seite 305 - IP Fellegi and AB Sunter. A theory for record linkage. Journal of the American Statistical Association, 64:1183-1210, 1969.
Seite 312 - Explaining answers from the semantic web: The inference web approach. Journal of Web Semantics 1(4), 397-413 (2004) 55.
Seite 311 - Wen-Syan Li and Chris Clifton: "Semantic Integration in Heterogeneous Databases Using Neural Networks,
Seite 320 - US), 2000. [52] [Wu and Palmer, 1994] Zhibiao Wu and Martha Palmer. Verb semantics and lexical selection. In Proc. 32nd Annual Meeting of the Association for Computational Linguistics (ACL), pages 133-138, Las Cruces (NM US), 1994.

Autoren-Profil (2007)

Jérôme Euzenat is senior research scientist at INRIA where he leads the Exmo team dedicated to computer-mediated exchanges of structured knowledge. He is supervising the "Heterogeneity" work package of the Knowledge web network of excellence which aims at structuring the European research community in ontology alignment and merging.

Pavel Shvaiko is a postdoc fellow at the Department of Information and Communication Technology (DIT) of the University of Trento (UniTn), Trento, Italy. In 2006, he finished his PhD on "Iterative Schema-based Semantic Matching". Currently, he works in a European research project on matching multiple schemas, classifications, ontologies as a solution to the semantic heterogeneity problem.

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