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Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective

In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears because of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To resolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a hybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzziness of natural language. We believe that the translation of Bayesian network model from the probabilistic framework to possibilistic one will allow a performance improvement of BNRM.