Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Applying Bayesian networks to information retrieval
Communications of the ACM
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Building Bayesian Network-Based Information Retrieval Systems
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Query expansion in information retrieval systems using a Bayesian network-based thesaurus
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Relevance feedback has been proven to be a very effective query modification technique that the user, by providing her/his relevance judgments to the Information Retrieval System, can use to retrieve more relevant documents. In this paper we are going to introduce a relevance feedback method for the Bayesian Network Retrieval Model, founded on propagating partial evidences in the underlying Bayesian network. We explain the theoretical frame in which our method is based on and report the results of a detailed set of experiments over the standard test collections Adi, CACM, CISI, Cranfield and Medlars.