Browse Theses

Information retrieval is concerned with selecting documents from a collection that will be of interest to a user with a stated information need or query. Research aimed at improving the performance of retrieval systems, that is, selecting those documents most likely to match the user's information need, remains an area of considerable theoretical and practical importance.

This dissertation describes a new formal retrieval model that uses probabilistic inference networks to represent documents and information needs. Retrieval is viewed as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the probability that a given document matches a query. This model generalizes several current retrieval models and provides a framework within which disparate information retrieval research results can be integrated.

To test the effectiveness of the inference network model, a retrieval system based on the model was implemented. Two test collections were built and used to compare retrieval performance with that of conventional retrieval models. The inference network model gives substantial improvements in retrieval performance with computational costs that are comparable to those associated with conventional retrieval models and which are feasible for large collections.