Abstract
We propose a probabilistic document retrieval model based on Bayesian networks. The network is used to compute the posterior probabilities of relevance of the documents in the collection given a query. These computations can be carried out efficiently, because of the specific network topology and conditional probability tables being considered, which allow the use of a fast and exact probabilities propagation algorithm. In the initial model, only direct relationships between the terms in the glossary and the documents that contain them are considered, giving rise to a Bayesian network with two layers. Next, we consider an extended model that also includes direct relationships between documents, using a network topology with three layers. We also report the results of a set of experiments with the two models, using several standard document collections.
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de Campos, L.M., Fernández-Luna, J.M., Huete, J.F. (2002). A Layered Bayesian Network Model for Document Retrieval. In: Crestani, F., Girolami, M., van Rijsbergen, C.J. (eds) Advances in Information Retrieval. ECIR 2002. Lecture Notes in Computer Science, vol 2291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45886-7_12
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DOI: https://doi.org/10.1007/3-540-45886-7_12
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