Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus
Abstract
Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. Using a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.
Cite
@article{arxiv.1301.7364,
title = {Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus},
author = {Luis M. de Campos and Juan M. Fernandez-Luna and Juan F. Huete},
journal= {arXiv preprint arXiv:1301.7364},
year = {2013}
}
Comments
Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)