000 | 04099nam a22005775i 4500 | ||
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001 | 978-981-19-1879-7 | ||
003 | DE-He213 | ||
005 | 20240423125332.0 | ||
007 | cr nn 008mamaa | ||
008 | 220613s2022 si | s |||| 0|eng d | ||
020 |
_a9789811918797 _9978-981-19-1879-7 |
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024 | 7 |
_a10.1007/978-981-19-1879-7 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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_aUYQE _2bicssc |
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_aCOM021030 _2bisacsh |
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_aUNF _2thema |
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_aUYQE _2thema |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aYe, Chen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aKnowledge Discovery from Multi-Sourced Data _h[electronic resource] / _cby Chen Ye, Hongzhi Wang, Guojun Dai. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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300 |
_aXII, 83 p. 14 illus., 9 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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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. | ||
650 | 0 | _aData mining. | |
650 | 0 | _aDatabase management. | |
650 | 0 |
_aArtificial intelligence _xData processing. |
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650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aData Science. |
700 | 1 |
_aWang, Hongzhi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aDai, Guojun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811918780 |
776 | 0 | 8 |
_iPrinted edition: _z9789811918803 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-19-1879-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c176833 _d176833 |