Entity Alignment
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 downst...
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| Những tác giả chính: | , , |
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
Springer Nature
2023
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| Những chủ đề: | |
| Truy cập trực tuyến: | ONIX_20231113_9789819942503_41 |
| Các nhãn: |
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| _version_ | 1869529542998097920 |
|---|---|
| author | Zhao, Xiang Zeng, Weixin Tang, Jiuyang |
| author_browse | Tang, Jiuyang Zeng, Weixin Zhao, Xiang |
| author_facet | Zhao, Xiang Zeng, Weixin Tang, Jiuyang |
| author_sort | Zhao, Xiang |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-121950 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1219502025-03-21T01:10:10Z 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 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. 2023-11-17T09:05:43Z 2023-11-17T09:05:43Z 2023-11-13T16:42:43Z 2023 book ONIX_20231113_9789819942503_41 OCN: 1407065938 https://library.oapen.org/handle/20.500.12657/85096 9789819942503 9789819942497 https://directory.doabooks.org/handle/20.500.12854/121950 eng Big Data Management open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/85096/1/978-981-99-4250-3.pdf https://library.oapen.org/bitstream/20.500.12657/85096/1/978-981-99-4250-3.pdf https://library.oapen.org/bitstream/20.500.12657/85096/1/978-981-99-4250-3.pdf Springer Nature Springer Nature Singapore 10.1007/978-981-99-4250-3 10.1007/978-981-99-4250-3 9fa3421d-f917-4153-b9ab-fc337c396b5a National University of Defense Technology d58bdc37-8afb-4810-b8e2-478379b1e8b0 9789819942503 9789819942497 Springer Nature Singapore 247 Singapore [...] open access |
| spellingShingle | Knowledge Graph Entity Alignment Knowledge Graph Alignment Knowledge Graph Matching Entity Matching Knowledge Fusion Data Integration Knowledge Graph Representation Learning Multi-Modal Knowledge Graph Zhao, Xiang Zeng, Weixin Tang, Jiuyang Entity Alignment |
| title | Entity Alignment |
| title_full | Entity Alignment |
| title_fullStr | Entity Alignment |
| title_full_unstemmed | Entity Alignment |
| title_short | Entity Alignment |
| title_sort | entity alignment |
| topic | Knowledge Graph Entity Alignment Knowledge Graph Alignment Knowledge Graph Matching Entity Matching Knowledge Fusion Data Integration Knowledge Graph Representation Learning Multi-Modal Knowledge Graph |
| topic_facet | Knowledge Graph Entity Alignment Knowledge Graph Alignment Knowledge Graph Matching Entity Matching Knowledge Fusion Data Integration Knowledge Graph Representation Learning Multi-Modal Knowledge Graph |
| url | ONIX_20231113_9789819942503_41 |
| work_keys_str_mv | AT zhaoxiang entityalignment AT zengweixin entityalignment AT tangjiuyang entityalignment |