04599cam a22005171i 4500 0000031261 OCoLC 20230921135901.0 m o d cr cnu|||unuuu 180110s2017 xxm ob u000 0 eng d 1026609800 9783319722092 (electronic bk.) 3319722093 (electronic bk.) 9783319722085 : $35.55 ZA4240 LAN 025000 bisacsh 025.042/7 23 Angermann, Heiko http://id.loc.gov/authorities/names/no2018042088, author. Taxonomy matching using background knowledge : linked data, semantic web and heterogeneous repositories / Heiko Angermann, Naeem Ramzan. Cham, Switzerland : Springer, 2017. Cham, Switzerland : Springer, [2017]. 1 online resource (xiv, 103 pages) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier polychrome rdacc http://rdaregistry.info/termList/RDAColourContent/1003 Includes bibliographical references. Part 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. Available to OhioLINK libraries. 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. 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. Semantic Web http://id.loc.gov/authorities/subjects/sh2002000569. Linked data http://id.loc.gov/authorities/subjects/sh2013002090. Information resources management http://id.loc.gov/authorities/subjects/sh85066147. Electronic books. Ramzan, Naeem http://id.loc.gov/authorities/names/nb2013004150, author. Ohio Library and Information Network http://id.loc.gov/authorities/names/no95058981. Printed edition 9783319722085. OhioLINK Connect to resource http://rave.ohiolink.edu/ebooks/ebc/9783319722092 SpringerLink Connect to resource http://link.springer.com/10.1007/978-3-319-72209-2 SpringerLink Connect to resource (off-campus) http://proxy.ohiolink.edu:9099/login?url=http://link.springer.com/10.1007/978-3-319-72209-2 190830 N 20230822095114.0 13243 13243 0 0 0 0 cutlib cutlib 2023-09-21 0 ZA 4240 ANG BK0052356 2023-09-21 00:00:00 c.045099 2023-09-21 BK