Semantic Social Network Analysis

Since its birth, the web provided many ways of interacting between us [6], revealing huge social network structures [17], a phenomenon amplified by web 2.0 applications [11]. Researchers extracted social networks from emails, mailinglist archives, hyperlink structure of homepages, cooccurrence of names in documents and from the digital traces created by web 2.0 application usages [9]. Facebook, LinkedIn or Myspace provide huge amounts of structured network data. The emergence of the semantic web approaches led researchers to build models of such online interactions using ontologies like FOAF, SIOC or SCOT. This paper starts with a brief state of the art on these enhanced RDF-based representations. We will see that the graphs built using these ontologies have a great potential that is not fully exploited so far. Then, we present a new framework for applying SNA to RDF representations of social data. In particular, the use of graph models underlying RDF and SPARQL extensions enables us to extract efficiently and to parameterize the classic SNA features directly from these representations.