Implementing Relevance Feedback in the Bayesian network retrieval model

Relevance Feedback consists in automatically formulating a new query according to the relevance judgments provided by the user after evaluating a set of retrieved documents. In this article, we introduce several relevance feedback methods for the Bayesian Network Retrieval Model. The theoretical frame on which our methods are based uses the concept of partial evidences, which summarize the new pieces of information gathered after evaluating the results obtained by the original query. These partial evidences are inserted into the underlying Bayesian network and a new inference process (probabilities propagation) is run to compute the posterior relevance probabilities of the documents in the collection given the new query. The quality of the proposed methods is tested using a preliminary experimentation with different standard document collections.