Domain ontologies provide conceptual formalization of domain knowledge. One contribution of this study consists in using them in conceptual Information Retrieval Systems (IRS), in particular to assess the relevance of documents with respect to a given query. For this matching process a model is proposed that incorporates both user preferences and semantic similarity measures attached to domain ontology. Our approach allows justifying the relevance of the results to the user, using visualization tools. As Information Retrieval is an iterative process, users may be involved in the control loop of the results relevancy to better specify their information needs. We propose to formalize ontology based relevance feedback using an objective function and a heuristic that efficiently builds a near optimal query. These approaches have been validated in two ways: automatic assessment based on standard collections of tests, and case studies involving experts from biomedical domain.
