Towards a Possibilistic Information Retrieval System Using Semantic Query Expansion

This paper presents a new possibilistic information retrieval system using semantic query expansion. The work is involved in query expansion strategies based on external linguistic resources. In this case, the authors exploited the French dictionary "Le Grand Robert". First, they model the dictionary as a graph and compute similarities between query terms by exploiting the circuits in the graph. Second, the possibility theory is used by taking advantage of a double relevance measure possibility and necessity between the articles of the dictionary and query terms. Third, these two approaches are combined by using two different aggregation methods. The authors also benefit from an existing approach for reweighting query terms in the possibilistic matching model to improve the expansion process. In order to assess and compare the approaches, the authors performed experiments on the standard 'LeMonde94' test collection.