A Bayesian approach for weighted ontologies and semantic search

Semantic similarity search is one of the most promising methods for improving the performance of retrieval systems. This paper presents a new probabilistic method for ontology weighting based on a Bayesian approach. In particular, this work addresses the semantic search method SemSim for evaluating the similarity among a user request and semantically annotated resources. Each resource is annotated with a vector of features (annotation vector), i.e., a set of concepts defined in a reference ontology. Analogously, a user request is represented by a collection of desired features. The paper shows, on the bases of a comparative study, that the adoption of the Bayesian weighting method improves the performance of the SemSim method.