Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus

TL;DR

This paper introduces a Bayesian network-based method for query expansion in information retrieval systems, aiming to improve retrieval effectiveness by automatically adding relevant terms based on learned term relationships.

Contribution

It presents a novel approach that constructs a Bayesian network thesaurus from document collections to enhance query expansion in IR systems.

Findings

Improved retrieval performance on standard test collections.

Effective modeling of term relationships using Bayesian networks.

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.