000			03647nam a22004095i 4500
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008			231025s2023 si | fo |||| 0|eng d
020			_a9789819942503
024	7		_a10.1007/978-981-99-4250-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2023 EB
100	1		_aZhao, Xiang _eautor _4http://id.loc.gov/vocabulary/relators/aut _9690132 _c(Computer scientist)
245	1	0	_aEntity Alignment : _bConcepts, Recent Advances and Novel Approaches _cby Xiang Zhao, Weixin Zeng, Jiuyang Tang
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
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-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.
988			_aSpringer_Computer_2023
650		7	_2embne _9162648 _aData mining
700	1		_9690133 _aZeng, Weixin _eautor
700	1		_9690134 _aTang, Jiuyang _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-4250-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2024 _dz _ean _zSI