Overview
- 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 alternative evaluations
- Describes issues of relevance to both researchers and practitioners
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Table of contents (6 chapters)
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Front Matter
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Introduction to Taxonomy Matching
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Front Matter
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Recent Matching Techniques, Algorithms, Systems, Evaluations, and Datasets
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Front Matter
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Taxonomy Heterogeneity Applications
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Front Matter
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Conclusions
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Taxonomy Matching Using Background Knowledge
Book Subtitle: Linked Data, Semantic Web and Heterogeneous Repositories
Authors: Heiko Angermann, Naeem Ramzan
DOI: https://doi.org/10.1007/978-3-319-72209-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-72208-5Published: 17 January 2018
Softcover ISBN: 978-3-319-89157-6Published: 06 June 2019
eBook ISBN: 978-3-319-72209-2Published: 08 January 2018
Edition Number: 1
Number of Pages: XIV, 103
Number of Illustrations: 14 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Pattern Recognition, Business Information Systems, Artificial Intelligence