Entity alignment : concepts, recent advances and novel approaches / Xiang Zhao, Weixin Zeng, Jiuyang Tang.
2023
QA76.9.D343 Z43 2023
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Details
Title
Entity alignment : concepts, recent advances and novel approaches / Xiang Zhao, Weixin Zeng, Jiuyang Tang.
ISBN
9789819942503 electronic book
9819942500 electronic book
9789819942497
9819942497
9819942527
9789819942527
9819942500 electronic book
9789819942497
9819942497
9819942527
9789819942527
Published
Singapore : Springer, 2023.
Language
English
Description
1 online resource (xi, 247 pages) : illustrations.
Item Number
10.1007/978-981-99-4250-3 doi
Call Number
QA76.9.D343 Z43 2023
Dewey Decimal Classification
006.3/12
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Open access.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 30, 2023).
Added Author
Series
Big data management, 2522-0187
Available in Other Form
Print version: 9789819942527
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Table of Contents
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.
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.