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001 978-981-99-4250-3
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020 _a9789819942503
_9978-981-99-4250-3
024 7 _a10.1007/978-981-99-4250-3
_2doi
050 4 _aQA76.76.E95
050 4 _aQ387-387.5
072 7 _aUYQE
_2bicssc
072 7 _aCOM025000
_2bisacsh
072 7 _aUYQE
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082 0 4 _a006.33
_223
100 1 _aZhao, Xiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEntity Alignment
_h[electronic resource] :
_bConcepts, Recent Advances and Novel Approaches /
_cby Xiang Zhao, Weixin Zeng, Jiuyang Tang.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXI, 247 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBig Data Management,
_x2522-0187
505 0 _aChapter 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.
506 0 _aOpen Access
520 _aThis 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-upresearch. 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.
650 0 _aExpert systems (Computer science).
650 0 _aData mining.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aKnowledge Based Systems.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Science.
700 1 _aZeng, Weixin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aTang, Jiuyang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819942497
776 0 8 _iPrinted edition:
_z9789819942510
776 0 8 _iPrinted edition:
_z9789819942527
830 0 _aBig Data Management,
_x2522-0187
856 4 0 _uhttps://doi.org/10.1007/978-981-99-4250-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
942 _cSPRINGER
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