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Taxonomy Matching Using Background Knowledge

Linked Data, Semantic Web and Heterogeneous Repositories

Language EnglishEnglish
Book Hardback
Book Taxonomy Matching Using Background Knowledge Heiko Angermann
Libristo code: 18622614
Publishers Springer International Publishing AG, January 2018
This important text/reference presents a comprehensive review of techniques for taxonomy matching, d... Full description
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This 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.

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About the book

Full name Taxonomy Matching Using Background Knowledge
Author Heiko Angermann, Naeem Ramzan
Language English
Binding Book - Hardback
Date of issue 2018
Number of pages 103
EAN 9783319722085
ISBN 3319722085
Libristo code 18622614
Publishers Springer International Publishing AG
Weight 349
Dimensions 155 x 235 x 12

Categories

Computing & Information Technology > Computer science > Artificial intelligence > Pattern recognition
Economics, Finance, Business & Management > Business & Management > Business mathematics & systems
Computing & Information Technology > Databases > Data mining
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