| 000 | 04863nam a22005775i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-38721-0 | ||
| 003 | DE-He213 | ||
| 005 | 20200421111155.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 131107s2013 gw | s |||| 0|eng d | ||
| 020 |
_a9783642387210 _9978-3-642-38721-0 |
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| 024 | 7 |
_a10.1007/978-3-642-38721-0 _2doi |
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| 050 | 4 | _aQA75.5-76.95 | |
| 072 | 7 |
_aUNH _2bicssc |
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| 072 | 7 |
_aUND _2bicssc |
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_aCOM030000 _2bisacsh |
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| 082 | 0 | 4 |
_a025.04 _223 |
| 100 | 1 |
_aEuzenat, J�er�ome. _eauthor. |
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| 245 | 1 | 0 |
_aOntology Matching _h[electronic resource] / _cby J�er�ome Euzenat, Pavel Shvaiko. |
| 250 | _a2nd ed. 2013. | ||
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
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| 300 |
_aXVII, 511 p. 103 illus., 1 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | _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. | |
| 520 | _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. | ||
| 650 | 0 | _aComputer science. | |
| 650 | 0 | _aInformation technology. | |
| 650 | 0 |
_aBusiness _xData processing. |
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| 650 | 0 | _aMathematical logic. | |
| 650 | 0 | _aInformation storage and retrieval. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aE-commerce. | |
| 650 | 1 | 4 | _aComputer Science. |
| 650 | 2 | 4 | _aInformation Storage and Retrieval. |
| 650 | 2 | 4 | _aInformation Systems Applications (incl. Internet). |
| 650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
| 650 | 2 | 4 | _aIT in Business. |
| 650 | 2 | 4 | _ae-Commerce/e-business. |
| 650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
| 700 | 1 |
_aShvaiko, Pavel. _eauthor. |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642387203 |
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-38721-0 |
| 912 | _aZDB-2-SCS | ||
| 942 | _cEBK | ||
| 999 |
_c53451 _d53451 |
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