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Possibilistic interest discovery from uncertain information in social networks

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the users’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.