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Leveraging Social data with Semantics


One of the challenges of social network analysis (SNA) is to understand and exploit on-line social interactions. Research in Semantic Web has provided models to leverage the richness of these interactions that we use to represent these social networks. Classical social network analysis methods have been applied to these semantic representations without fully exploiting their rich expressiveness. Furthermore, we can extend the representation of social links thanks to the semantic relationships found in the vocabularies shared by the members of the social networks. These " enriched " representations of social networks, combined with a similar enrichment of the semantics of the meta-data attached to the shared resources, will allow the elaboration of " shared knowledge graphs ". In this paper we present our approach to analyse such semantic social networks and capture collective intelligence from collaborative interactions.
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Dates and versions

hal-01154486 , version 1 (22-05-2015)


  • HAL Id : hal-01154486 , version 1


Guillaume Erétéo, Michel Buffa, Fabien Gandon, Mylène Leitzelman, Freddy Limpens. Leveraging Social data with Semantics. W3C Workshop on the Future of Social Networking, W3C, Jan 2009, Barcelone, Spain. ⟨hal-01154486⟩
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