Data-Driven Semantic Concept Analysis for User Profile Learning in 3G Recommender Systems

The paper presents Semantic Concept Analysis (SCA) framework intended for automatic data-driven design of actionable ontology specifying mobile device user's personal interest's hierarchy together with dual structure reflecting the user's preferences over these interests. The framework integrates known technique for semi-automatic ontology design exploiting DBpedia and Wikipedia categories, on the one hand, and the data-driven Formal Concept Analysis (FCA), on the other one. The framework implements a kind of machine-learning approach integrating algebraic and statistical models of data and knowledge structured as s a pair of dual concept semi-lattices. The proposed technology implementing SCA framework basic ideas is validated experimentally through its software prototyping and subsequent computer experimentation using natural language text data sample.