LEADER 04397oam 2200493 450 001 9910437582403321 005 20190911112726.0 010 $a3-642-38721-7 024 7 $a10.1007/978-3-642-38721-0 035 $a(OCoLC)864999776 035 $a(MiFhGG)GVRL6VBI 035 $a(EXLCZ)993710000000031267 100 $a20131021d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aOntology matching /$fJerome Euzenat, Pavel Shvaiko 205 $a2nd ed. 2013. 210 1$aHeidelberg [Germany] :$cSpringer,$d2013. 215 $a1 online resource (xvii, 511 pages) $cillustrations (some color) 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a3-642-38720-9 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Part I The matching problem -- Applications -- The matching problem -- Methodology -- Part II Ontology matching techniques -- Classifications of ontology matching techniques -- Basic similarity measures -- Global matching methods -- Matching strategies -- Part III Systems and evaluation -- Overview of matching systems -- Evaluation of matching systems -- Part IV Representing, explaining, and processing alignments -- Frameworks and formats: representing alignments -- User involvement -- Processing alignments -- Part V Conclusions -- Conclusions -- Appendix A: Legends of figures -- Appendix B: Running example -- Appendix C: Exercises -- Appendix D: Solution to exercises. 330 $aOntologies 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 than 150 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. 606 $aOntologies (Information retrieval) 606 $aSemantic integration (Computer systems) 615 0$aOntologies (Information retrieval) 615 0$aSemantic integration (Computer systems) 676 $a004 676 $a005.131 676 $a005.7 676 $a006.3 700 $aEuzenat$b Jérôme$4aut$4http://id.loc.gov/vocabulary/relators/aut$0601882 702 $aShvaiko$b Pavel 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910437582403321 996 $aOntology Matching$92532024 997 $aUNINA