Entity Alignment

ISBN-13: 9789819942497 / Angielski / Twarda / 2023

Xiang Zhao;Weixin Zeng;Jiuyang Tang

Opis

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.

Spis treści

Chapter 1. Introduction to Entity Alignment .- Chapter 2. State-of-the-art Approaches and Categorization.- Chapter 3. Recent Advance in Representation Learning.- Chapter 4. Recent Advance in Alignment Inference.- Chapter 5. Experimental Survey and Evaluation.- Chapter 6. Large-scale Entity Alignment.- Chapter 7. Long-tail Entity Alignment.- Chapter 8. Weakly-supervised Entity Alignment.- Chapter 9. Unsupervised Entity Alignment.- Chapter 10. Multimodal Entity Alignment.

O autorze

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.