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    Entity Alignment

    Concepts, Recent Advances and Novel Approaches

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    Author(s)
    Zhao, Xiang
    Zeng, Weixin
    Tang, Jiuyang
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/85096
    Keywords
    Knowledge Graph; Entity Alignment; Knowledge Graph Alignment; Knowledge Graph Matching; Entity Matching; Knowledge Fusion; Data Integration; Knowledge Graph Representation Learning; Multi-Modal Knowledge Graph
    DOI
    10.1007/978-981-99-4250-3
    ISBN
    9789819942503, 9789819942497, 9789819942503
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Singapore, 2023
    Grantor
    • National University of Defense Technology - [...]
    Imprint
    Springer Nature Singapore
    Series
    Big Data Management,
    Pages
    247
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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