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  • Book
  • Open Access
  • © 2023

Entity Alignment

Concepts, Recent Advances and Novel Approaches

  • 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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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xi
  2. Concept and Categorization

    1. Front Matter

      Pages 1-1
    2. Introduction to Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 3-13Open Access
    3. State-of-the-Art Approaches

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 15-48Open Access
  3. Recent Advances

    1. Front Matter

      Pages 49-49
    2. Recent Advance of Representation Learning Stage

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 51-75Open Access
    3. Recent Advance of Alignment Inference Stage

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 77-112Open Access
  4. Novel Approaches

    1. Front Matter

      Pages 113-113
    2. Large-Scale Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 115-159Open Access
    3. Long-Tail Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 161-184Open Access
    4. Weakly Supervised Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 185-206Open Access
    5. Unsupervised Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 207-227Open Access
    6. Multimodal Entity Alignment

      • Xiang Zhao, Weixin Zeng, Jiuyang Tang
      Pages 229-247Open Access

About this book

This open access book systematically investigates the topic of entity alignment, which aims to detect equivalent entities that are located in different knowledge graphs. Entity alignment represents an essential step in enhancing the quality of knowledge graphs, and hence is of significance to downstream applications, e.g., question answering and recommender systems. Recent years have witnessed a rapid increase in the number of entity alignment frameworks, while the relationships among them remain unclear. This book aims to fill that gap by elaborating the concept and categorization of entity alignment, reviewing recent advances in entity alignment approaches, and introducing novel scenarios and corresponding solutions.

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-up research. 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.


Authors and Affiliations

  • Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China

    Xiang Zhao, Weixin Zeng, Jiuyang Tang

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

Buy it now

Buying options

Softcover Book USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access