Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus
Luis de Campos, Juan Fernandez-Luna, Juan Huete
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.
Keywords: Bayesian networks, information retrieval, learning algorithms, thesaurus, query expan
PDF Link: /papers/98/p53-de_campos.pdf
AUTHOR = "Luis de Campos
and Juan Fernandez-Luna and Juan Huete",
TITLE = "Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "53--60"