Personalizing and Improving Tag-Based Search in Folksonomies

International audienceRecently, the approaches that combine semantic web ontologies and web 2.0 technologies have constituted a significant research field. We present in this paper an original approach concerning a technology that has recognized a great popularity in these recent years, we talk about folksonomies. Our aim in this contribution is propose new technique for the Social Semantic Web technologies in order to see how we can overcome the problem of tags' ambiguity automatically in folksonomies even when we choose representing these latter with ontologies. We'll also illustrate how we can enrich any folksonomy by a set of pertinent data to improve and facilitate the resources' retrieval in these systems; all this with tackling another problem, we speak about spelling variations. Among the powerful technologies of Web 2.0, we find folksonomies, this term has recently appeared on the net to describe a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content. Ontologies which constitute the backbone of semantic web contribute significantly in solving the problems of semantics during the definition and the search of information. However even with the strong points of folksonomies and ontologies; their combination together still suffers from some problems. As examples we can cite the problem of tags' ambiguity and spelling variations (or Synonymy) in folksonomies. Our goal in this contribution is to show how we can exploit the power of social interactions between the folksonomy's members in order to extract the meaning of terms and overcome the problems of tags' ambiguity and spelling variations. Also we will try to show how we can use the principle of rules-based systems with ontologies for helping our system to enhance automatically the folksonomy by relevant facts can increase the data available within our system wit