The primary system functionalities are outlined from the user perspective, followed by a description of the document database used in the system. The technologies and linguistic resources used in the various system components are then described in detail. We investigate the application of a novel relevance ranking technique, cover density ranking, to the requirements of Web-based information retrieval, where a typical query consists of a few search terms and a typical result consists of a page indicating several potentially relevant documents. Traditional ranking methods for information retrieval, based on term and inverse document frequencies, have been found to work poorly in this context. Under the cover density measure, ranking is based on term proximity and cooccurrence.
Experimental comparisons show performance that compares favorably with previous work.
In this model, we treat documents as 'disorders' and user information needs as 'manifestations', and a competitive activation mechanism is used which converges to a set of documents that best explain the given user information needs. By combining the ideas of Bayesian inferencing and diagnostic inferencing using parsimonious covering theory, this model removes many difficulties of direct application of Bayesian inference, such as the unrealistically large number of conditional probabilities required in the knowledge base, the computational complexity, and certain unreasonable independence assumptions. Also, Bayesian inference strengthens the parsimonious covering theory by providing a likelihood measure which can be used to rank documents as well as to guide the retrieval to the most likely set of documents. We also incorporate thesaurus information to provide semantic relevance among the index terms. Our experimental results using 4 standard document collections demonstrate the efficiency and the retrieval effectiveness of the thesaurus-based model, comparable to or better than that of various information retrieval models reported in the literature.