Belief Function Model for information retrieval

Abstract

The Belief Function Model for automatic indexing and ranking of documents with respect to a given user query is proposed. The model is based on a controlled vocabulary, like a thesaurus, and on term frequencies in each document. Descriptors in this vocabulary are terms chosen from among their synonyms to be used as index terms. A descriptor can have a subset of broader descriptors, a subset of narrower descriptors, and a subset of related descriptors. Thus, descriptors are not mutually exclusive and naive probabilistic models are inadequate for handling them. However, a belief function can still be defined over a thesaurus of descriptors. Belief functions over the descriptors can represent a document or a user query. We can compute the agreement between a document belief function and a query belief function. Therefore, we propose that the set of documents be ranked according to their agreement with the given user query. We show that the Belief Function Model is wider in scope than the Standard Vector Space Model. © 1993 John Wiley & Sons, Inc.