Leveraging social network analysis with topic models and the Semantic Web extended

  • Authors:
  • Sebastián A. Ríos;Felipe Aguilera;Francisco Bustos;Tope Omitola;Nigel Shadbolt

  • Affiliations:
  • Industrial Engineering Department, University of Chile, AV. República 701, Santiago, Chile;Computer Science Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: {faguiler,fbustos}@dcc.uchile.cl;Computer Science Department, University of Chile, AV. República 701, Santiago, Chile. E-mail: {faguiler,fbustos}@dcc.uchile.cl;WAIS, School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK. E-mail: {tobo,nrs}@ecs.soton.ac.uk;WAIS, School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK. E-mail: {tobo,nrs}@ecs.soton.ac.uk

  • Venue:
  • Web Intelligence and Agent Systems - Web Intelligence and Communities
  • Year:
  • 2013

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Abstract

Research in the Semantic Web, especially in modeling virtual communities, has provided models useful to represent the richness of these social network interactions. The SIOC Semantically-Interlinked Online Communities vocabulary provides concepts and properties that can be used to describe information from online communities e.g., message boards, wikis, weblogs, etc.. However, the SIOC ontology does not consider social aspects nor the higher order semantics hidden in linkages between community members. This paper describes SIOC-SNA-DM, an extension of the SIOC vocabulary. SIOC-SNA-DM's model is tri-partite, consisting of People, Policies, and Purposes which are social aspects observable in most social communities. A challenge to using our model is how to populate these aspects, since higher order semantics from interactions need to be extracted. Thus, we explain how this population is done with advanced text mining using a latent semantic technique over a large virtual community called Plexilandia.cl with more than 2500 musicians working on the site.Our previous work, in this area, has shown how including these social aspects help to outperform results generated by state-of-the-art techniques. One of the novelties of this present work is the introduction and the elucidation of SIOC-SNA-DM, and how to populate the ontology in order to support the social aspects needed to enhance results of Social Network Mining techniques.