| 000 | 03344nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01907-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240730165155.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2015 sz | s |||| 0|eng d | ||
| 020 |
_a9783031019074 _9978-3-031-01907-4 |
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| 024 | 7 |
_a10.1007/978-3-031-01907-4 _2doi |
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| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aUNF _2bicssc |
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_a006.312 _223 |
| 100 | 1 |
_aWang, Chi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987745 |
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| 245 | 1 | 0 |
_aMining Latent Entity Structures _h[electronic resource] / _cby Chi Wang, Jiawei Han. |
| 250 | _a1st ed. 2015. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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| 300 |
_aXI, 147 p. _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 |
_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 |
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| 505 | 0 | _aAcknowledgments -- Introduction -- Hierarchical Topic and Community Discovery -- Topical Phrase Mining -- Entity Topical Role Analysis -- Mining Entity Relations -- Scalable and Robust Topic Discovery -- Application and Research Frontier -- Bibliography -- Authors' Biographies. | |
| 520 | _aThe "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3)entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions. | ||
| 650 | 0 |
_aData mining. _93907 |
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| 650 | 0 |
_aStatistics . _931616 |
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| 650 | 1 | 4 |
_aData Mining and Knowledge Discovery. _987747 |
| 650 | 2 | 4 |
_aStatistics. _914134 |
| 700 | 1 |
_aHan, Jiawei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _92109 |
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| 710 | 2 |
_aSpringerLink (Online service) _987749 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031007798 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031030352 |
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_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 _987750 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01907-4 |
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| 942 | _cEBK | ||
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