Possibilistic Network based Information Retrieval Model

Abstract

This paper proposes a new Information Retrieval Model based on Possibilistic Networks. The model structure integrates most relevant term to term dependence relationships. The approach used to extract the set of these dependencies focuses on local dependencies between terms within each document. The relevance of a document to a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a document is relevant to a query, whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. These two measures are also used for quantifying terms-terms links and terms-documents links. Experiments carried out on three standard document collections show the effectiveness of the model.