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008			221015s2022 si | s |||| 0|eng d
020			_a9789811918797
024	7		_a10.1007/978-981-19-1879-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2022 EB
100	1		_aYe, Chen, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684999 _d1985-
245	1	0	_aKnowledge Discovery from Multi-Sourced Data _cby Chen Ye, Hongzhi Wang, Guojun Dai
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XII, 83 páginas) _b14 ilustraciones, 9 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_a1. Introduction -- 2. Functional-dependency-based truth discovery for isomorphic data -- 3. Denial-constraint-based truth discovery for isomorphic data -- 4. Pattern discovery for heterogeneous data -- 5. Deep fact discovery for text data.
520			_aThis book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to "label" or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.
988			_aSpringer_Computer_2022
650		7	_2embne _9162648 _aData mining
700			_aWang, Hongzhi. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100974
700	1		_aDai, Guojun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685000
776	0	8	_iPrinted edition: _z9789811918780
776	0	8	_iPrinted edition: _z9789811918803
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-1879-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI