Probabilistic Information Retrieval Based on Conceptual Overlap in Semantic Web Ontologies

Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies. Degrees of overlap indicate how well an individual 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 understandable 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 document about Germany relevant even if the word ’Europe’ is not mentioned in it. With the help of an appropriate geographical ontology also an information retrieval system could easily make the above inference. Ontologies have in fact been used in a number of information retrieval system in recent years [14, 9, 10]. Ontologies are based on crisp logic. In the real world, however, relations between entities often include subtleties that are difficult to express in crisp ontologies. For example, most of the birds in Antarctica are penguins. Thus, if a document is annotated with the concepts ’Antarctica’ and ’Bird’, a human will make the inference that the document is related to Penguins. RDF(S) [2] and OWL [1] ontologies do not provide good means to make this kind of inferences. Also the information system itself is a source of uncertainty. The annotation, i.e. the indexing, of the documents is often inexact or uncertain. For example, if we have a geographical ontology about the countries and areas of North-America, and we want to index photographs using it, then it is most likely that in some point we will encounter a photograph the origin of which we do not know exactly. Typically the photograph will be annotated with the concept North-America. This kind of uncertainty may also be resulted from ontology merging or evolution. When a human information searcher will encounter the annotation, she will infere that there is a high probability that the photograph is taken in the U.S.A, because it is by far the largest country in NorthAmerica. It would be very useful if also an information retrieval system could make the same kind of inferences and use them when constructing the result sets for queries. Notice that in the above examples the knowledge of degrees of overlap and coverage between concepts is essential for succeeding in the information retrieval task. This paper presents a new method to approach the above problems. The method is based on the modeling of degrees of overlap between concepts. In the following we first introduce the principles of our method. Then a notation that enables the representation of degrees of overlap between concepts in an ontology is presented after which a method for doing inferences based on the notation will be described. Then our implementation of the method is discussed, and finally conclusions are drawn and related work discussed. 2 MODELING UNCERTAINTY IN ONTOLOGIES World 37*23 = 851 Europe 15*23 = 345