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.
Table des matières
The matching problem.- Applications.- The matching problem.- Ontology matching techniques.- Classifications of ontology matching techniques.- Basic techniques.- Matching strategies.- Systems and evaluation.- Overview of matching systems.- Evaluation of matching systems.- Representing, explaining, and processing alignments.- Frameworks and formats: representing alignments.- Explaining alignments.- Processing alignments.- Conclusions.- Conclusions.
A propos de l’auteur
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 (Uni Tn), Trento, Italy. In 2006, he finished his Ph D 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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