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

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