Big Data Management Entity Alignment - Xiang Zhao Weixin Zeng Jiuyang Tang Kartoniert (TB)

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 a lignment 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.