Probabilistic information retrieval based on conceptual overlap in semantic web ontologies

Abstract. Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Se-mantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to ap-proach the problem. In our method, degrees of subsumption, i.e., overlap be-tween concepts can be modeled and computed efficiently using Bayesian net-works based on RDF(S) ontologies. Degrees of overlap indicate how well an in-dividual data item matches the query concept, which can be used as a well-defined measure of relevance in information retrieval tasks. 1 ONTOLOGIES AND INFORMATION RETRIEVAL A key reason for using ontologies in information retrieval systems, is that they enable the representation of background knowledge about a domain in a machine understand-able format. Humans use background knowledge heavily in information retrieval tasks [8]. For example, if a person is searching for documents about Europe she will use her background knowledge about European countries in the task. She will find a doc