Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories

Angermann, Heiko; Ramzan, Naeem

ISBN 10: 331989157X ISBN 13: 9783319891576

Editorial: Springer, 2019

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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.

Acerca del autor:

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.

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T�tulo: Taxonomy Matching Using Background Knowledge...
Editorial: Springer
A�o de publicaci�n: 2019
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Taxonomy Matching Using Background Knowledge

Heiko Angermann|Naeem Ramzan

Publicado por Springer International Publishing, 2019

ISBN 10: 331989157X ISBN 13: 9783319891576

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides in-depth coverage of the state of the art in taxonomy matching, and the related fields of ontology matching and schema matching Reviews matching strategies, matching algorithms, matching systems and OAEI campaigns, in addition to alterna. N� de ref. del art�culo: 294884918

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Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories

Heiko Angermann, Naeem Ramzan

Publicado por Springer 2019-06-06, 2019

ISBN 10: 331989157X ISBN 13: 9783319891576

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Taxonomy Matching Using Background Knowledge

Naeem Ramzan

Publicado por Springer International Publishing, Springer International Publishing Jun 2019, 2019

ISBN 10: 331989157X ISBN 13: 9783319891576

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Taschenbuch. Condici�n: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. N� de ref. del art�culo: 9783319891576

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Taxonomy Matching Using Background Knowledge : Linked Data, Semantic Web and Heterogeneous Repositories

Naeem Ramzan

Publicado por Springer International Publishing, Springer International Publishing, 2019

ISBN 10: 331989157X ISBN 13: 9783319891576

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

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Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de ref. del art�culo: 9783319891576

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Taxonomy Matching Using Background Knowledge

Naeem Ramzan

Publicado por Springer International Publishing Jun 2019, 2019

ISBN 10: 331989157X ISBN 13: 9783319891576

Nuevo Taschenbuch

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch. N� de ref. del art�culo: 9783319891576

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