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oapen-20.500.12657-850962023-11-15T09:17:26Z Entity Alignment Zhao, Xiang Zeng, Weixin Tang, Jiuyang Knowledge Graph Entity Alignment Knowledge Graph Alignment Knowledge Graph Matching Entity Matching Knowledge Fusion Data Integration Knowledge Graph Representation Learning Multi-Modal Knowledge Graph bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMB Algorithms & data structures bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence 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. 2023-11-13T16:42:43Z 2023-11-13T16:42:43Z 2023 book ONIX_20231113_9789819942503_41 9789819942503 9789819942497 https://library.oapen.org/handle/20.500.12657/85096 eng Big Data Management application/pdf n/a 978-981-99-4250-3.pdf https://link.springer.com/978-981-99-4250-3 Springer Nature Springer Nature Singapore 10.1007/978-981-99-4250-3 10.1007/978-981-99-4250-3 6c6992af-b843-4f46-859c-f6e9998e40d5 d58bdc37-8afb-4810-b8e2-478379b1e8b0 9789819942503 9789819942497 Springer Nature Singapore 247 Singapore [...] National University of Defense Technology NUDT open access
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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.
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