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(Ebook) Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories by Heiko Angermann,Naeem Ramzan (auth.) ISBN 9783319722085, 9783319722092, 3319722085, 3319722093

  • SKU: EBN-6842826
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Instant download (eBook) Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories after payment.
Authors:Heiko Angermann,Naeem Ramzan (auth.)
Pages:108 pages.
Year:2017
Editon:1
Publisher:Springer International Publishing
Language:english
File Size:1.97 MB
Format:pdf
ISBNS:9783319722085, 9783319722092, 3319722085, 3319722093
Categories: Ebooks

Product desciption

(Ebook) Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories by Heiko Angermann,Naeem Ramzan (auth.) ISBN 9783319722085, 9783319722092, 3319722085, 3319722093

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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