Overview
- Presents the concept and categorization of entity alignment between knowledge graphs
- Provides a comprehensive overview and detailed evaluations of state-of-the-art entity alignment approaches
- Introduces novel entity alignment scenarios and corresponding solutions
- This book is open access, which means that you have free and unlimited access
Part of the book series: Big Data Management (BIGDM)
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About this book
Specifically, the book includes comprehensive evaluations and detailed analyses of state-of-the-art entity alignment approaches and strives to provide a clear picture of the strengths and weaknesses of the currently available solutions, so as to inspire follow-upresearch. In addition, it identifies novel entity alignment scenarios and explores the issues of large-scale data, long-tail knowledge, scarce supervision signals, lack of labelled data, and multimodal knowledge, offering potential directions for future research.
The book offers a valuable reference guide for junior researchers, covering the latest advances in entity alignment, and a valuable asset for senior researchers, sharing novel entity alignment scenarios and their solutions. Accordingly, it will appeal to a broad audience in the fields of knowledge bases, database management, artificial intelligence and big data.
Keywords
Table of contents (9 chapters)
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Concept and Categorization
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Recent Advances
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Novel Approaches
Authors and Affiliations
About the authors
Xiang Zhao is a Professor with Laboratory for Big Data and Decision, National University of Defense Technology (NUDT), China. In 2013, he received his PhD in Computer Science and Engineering from the University of New South Wales (UNSW), Australia. His research interests include knowledge graphs, advanced data management, and information retrieval. He has published over 100 refereed papers in leading journals and conference proceedings. Prof. Zhao has served on the technical program committees of various international conferences, including VLDB, ICDE, KDD, ACL and AAAI, and as program co-chair of WISA 2023, vice program co-chair of IEEE BigData 2023, area co-chair of CCKS 2022, tutorial co-chair of APWeb-WAIM 2022, etc. He was selected for the ACM SIGMOD China Rising Star award in 2018.
Weixin Zeng is a Lecturer with Laboratory for Big Data and Decision, National University of Defense Technology (NUDT), China. His research interests include knowledge graphs, entity alignment and entity linking. He has published over 30 papers in leading journals and conference proceedings, including ACM TOIS, SIGIR, The VLDB Journal, IEEE TKDE and ICDE. He has served as a program committee member or reviewer for various international events, including ACL, AAAI and TKDE.
Jiuyang Tang is a Professor with Laboratory for Big Data and Decision, National University of Defense Technology (NUDT), China. He received his PhD degree from the NUDT in 2006. His research interests include knowledge graphs and advanced data analytics. He has published over 60 papers in leading journals and conference proceedings, including ACM TOIS, SIGIR, IEEE TKDE and The VLDB Journal.
Bibliographic Information
Book Title: Entity Alignment
Book Subtitle: Concepts, Recent Advances and Novel Approaches
Authors: Xiang Zhao, Weixin Zeng, Jiuyang Tang
Series Title: Big Data Management
DOI: https://doi.org/10.1007/978-981-99-4250-3
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2023
Hardcover ISBN: 978-981-99-4249-7Published: 26 October 2023
Softcover ISBN: 978-981-99-4252-7Published: 26 October 2023
eBook ISBN: 978-981-99-4250-3Published: 25 October 2023
Series ISSN: 2522-0179
Series E-ISSN: 2522-0187
Edition Number: 1
Number of Pages: XI, 247
Number of Illustrations: 1 b/w illustrations
Topics: Knowledge based Systems, Data Mining and Knowledge Discovery, Data Structures and Information Theory, Artificial Intelligence