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010   $a3-319-72209-3
024 7 $a10.1007/978-3-319-72209-2
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035   $a(DE-He213)978-3-319-72209-2
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100   $a20180108d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aTaxonomy Matching Using Background Knowledge $eLinked Data, Semantic Web and Heterogeneous Repositories  /$fby Heiko Angermann, Naeem Ramzan
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XIV, 103 p. 14 illus.) 
311   $a3-319-72208-5 
320   $aIncludes bibliographical references and index.
327   $aPart I: Introduction to Taxonomy Matching -- Background Taxonomy Matching -- Background of Taxonomic Heterogeneity.- Part II: Recent Matching Techniques, Algorithms, Systems, Evaluations, and Datasets -- Matching Techniques, Algorithms, and Systems -- Matching Evaluations and Datasets.- Part III: Taxonomy Heterogeneity Applications -- Related Areas.- Part IV: Conclusions -- Conclusions.
330   $aThis important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches. Different methods are investigated in accordance with the criteria of the Ontology Alignment Evaluation Initiative (OAEI). The text also highlights promising developments and innovative guidelines, to further motivate researchers and practitioners in the field. Topics and features: Discusses the fundamentals and the latest developments in taxonomy matching, including the related fields of ontology matching and schema matching Reviews next-generation matching strategies, matching algorithms, matching systems, and OAEI campaigns, as well as alternative evaluations Examines how the latest techniques make use of different sources of background knowledge to enable precise matching between repositories Describes the theoretical background, state-of-the-art research, and practical real-world applications Covers the fields of dynamic taxonomies, personalized directories, catalog segmentation, and recommender systems This stimulating book is an essential reference for practitioners engaged in data science and business intelligence, and for researchers specializing in taxonomy matching and semantic similarity assessment. The work is also suitable as a supplementary text for advanced undergraduate and postgraduate courses on information and metadata management. Dr. Heiko Angermann is an e-commerce, enterprise content management, and omni/multi-channel consultant, and the Head of Project Management at an e-commerce consulting house located in Nuremberg, Germany. Prof. Naeem Ramzan is a full Professor of Computing Engineering in the School of Engineering and Computing at the University of West of Scotland, Paisley, UK. His other publications include the successful Springer title Social Media Retrieval.
606   $aData mining
606   $aPattern recognition
606   $aManagement information systems
606   $aArtificial intelligence
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aBusiness Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/522030
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aData mining.
615  0$aPattern recognition.
615  0$aManagement information systems.
615  0$aArtificial intelligence.
615 14$aData Mining and Knowledge Discovery.
615 24$aPattern Recognition.
615 24$aBusiness Information Systems.
615 24$aArtificial Intelligence.
676   $a006.4
700   $aAngermann$b Heiko$4aut$4http://id.loc.gov/vocabulary/relators/aut$0996351
702   $aRamzan$b Naeem$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910255453903321
996   $aTaxonomy Matching Using Background Knowledge$92543132
997   $aUNINA