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020 _a9783031794599
_9978-3-031-79459-9
024 7 _a10.1007/978-3-031-79459-9
_2doi
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072 7 _aPB
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072 7 _aMAT000000
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082 0 4 _a510
_223
100 1 _aOmitola, Tope.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983547
245 1 0 _aSocial Semantic Web Mining
_h[electronic resource] /
_cby Tope Omitola, Sebastián A. Ríos, John G. Breslin.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 138 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 extensionsto 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).
650 0 _aMathematics.
_911584
650 0 _aInternet programming.
_935503
650 0 _aApplication software.
_983549
650 0 _aComputer networks .
_931572
650 0 _aOntology.
_95277
650 1 4 _aMathematics.
_911584
650 2 4 _aWeb Development.
_935505
650 2 4 _aComputer and Information Systems Applications.
_983552
650 2 4 _aComputer Communication Networks.
_983553
650 2 4 _aOntology.
_95277
700 1 _aRíos, Sebastián A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983555
700 1 _aBreslin, John G.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983557
710 2 _aSpringerLink (Online service)
_983561
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031794582
776 0 8 _iPrinted edition:
_z9783031794605
830 0 _aSynthesis Lectures on Data, Semantics, and Knowledge,
_x2691-2031
_983563
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79459-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85528
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