Applying Bayesian networks to information retrieval

Abstract

Information retrieval (IR) is the identification of documents or other units of information in a collection that are relevant to a particular information need. An information need is a set of questions to which someone would like to find an answer. Here are some examples of IR tasks: finding articles in the New York Times that discuss the Iran-Contra affair; searching the recent postings in a Usenet newsgroup for references to a particular model of personal computer; finding the entries referring to butterflies in an online CD-ROM encyclopedia.

A risk minimization framework for information retrieval

“…The estimation of relevance is based on the computation of the conditional probability that the query is satisfied given that the document is observed. Other similar uses of Bayesian belief network in retrieval have been presented in (Fung and Favero, 1995;Ribeiro and Muntz, 1996;Ribeiro-Neto et al, 2000). Kwok's network model may also be considered as performing a probabilistic inference (Kwok, 1995), though it is based on spread activation.…”

Information Retrieval Using Bayesian Networks

“…In this chapter we focus on Bayesian networks and their application to web-based systems, which are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence. They model stochastic processes such as medical systems, military scenarios, academic advising, information retrieval [1], [2] system troubleshooting [3], [4], language understanding, business and in many more [8].…”

A Layered Bayesian Network Model for Document Retrieval

“…An IR model is a specification about how to represent documents and queries (formal statements of user's information needs), and how to compare them, whereas an IR system is the computer software that implements a model. Probabilistic IR models [4,9,12] use probability theory to deal with the intrinsic uncertainty with which IR is pervaded [3]. Also founded on probabilistic methods, Bayesian networks [5] have been proven to be a good model to manage uncertainty, even in the IR environment, where they have already been successfully applied as an extension of probabilistic IR models [13,14,7].…”