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008			220601s2015 sz | o |||| 0|eng d
020			_a9783031794599
024	7		_a10.1007/978-3-031-79459-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aZA4240 _b2015 EB
100	1		_aOmitola, Tope _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688424
245	1	0	_aSocial Semantic Web Mining _cby Tope Omitola, Sebastián Ríos, John Breslin
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XV, 138 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Semantics and Knowledge _x2691-2031
505	0		_aAcknowledgments -- Grant Aid -- Introduction and the Web -- Web Mining -- The Social Web -- The Semantic Web -- The Social Semantic Web -- Social Semantic Web Mining -- Social Semantic Web Mining of Communities -- Social Semantic Web Mining of Groups -- Social Semantic Web Mining of Users -- Conclusions -- Bibliography -- Authors' Biographies .
520			_aThe past ten years have seen a rapid growth in the numbers of people signing up to use Web-based social networks (hundreds of millions of new members are now joining the main services each year) with a large amount of content being shared on these networks (tens of billions of content items are shared each month). With this growth in usage and data being generated, there are many opportunities to discover the knowledge that is often inherent but somewhat hidden in these networks. Web mining techniques are being used to derive this hidden knowledge. In addition, the Semantic Web, including the Linked Data initiative to connect previously disconnected datasets, is making it possible to connect data from across various social spaces through common representations and agreed upon terms for people, content items, etc. In this book, we detail some current research being carried out to semantically represent the implicit and explicit structures on the Social Web, along with the techniques being used to elicit relevant knowledge from these structures, and we present the mechanisms that can be used to intelligently mesh these semantic representations with intelligent knowledge discovery processes. We begin this book with an overview of the origins of the Web, and then show how web intelligence can be derived from a combination of web and Social Web mining. We give an overview of the Social and Semantic Webs, followed by a description of the combined Social Semantic Web (along with some of the possibilities it affords), and the various semantic representation formats for the data created in social networks and on social media sites. Provenance and provenance mining is an important aspect here, especially when data is combined from multiple services. We will expand on the subject of provenance and especially its importance in relation to social data. We will describe extensions to social semantic vocabularies specifically designed for community mining purposes (SIOCM). In the last three chapters, we describe how the combination of web intelligence and social semantic data can be used to derive knowledge from the Social Web, starting at the community level (macro), and then moving through group mining (meso) to user profile mining (micro).
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9163805 _aWeb semántica
650		7	_2embne _9673879 _aInternautas
650		7	_2embne _9162648 _aData mining
700	1		_aRíos, Sebastián A. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688425
700	1		_aBreslin, John G. _q(John Gerard) _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688426
776	0	8	_iPrinted edition: _z9783031794582
776	0	8	_iPrinted edition: _z9783031794605
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79459-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI