Semantic and Bayesian Profiling Services for Textual Resource Retrieval
This paper presents an integrated approach to textual resource retrieval, which combines logical inference services with user profiles, in which a structured representation of the user interests is maintained. Learning is performed on documents which have been disambiguated by exploiting the WordNet lexical database, in an attempt to discover concepts describing user interests. The proposed approach relies on several additional features compared to classical lexical knowledge systems, including: structured user recommendation, numeric value management, definition of strict and negotiable constraints and keywords to retrieve potential interesting resources w.r.t. both user request and profile.