A competition-based connectionist model for information retrieval using a merged thesaurus
Proceedings of the third international conference on Information and knowledge management - CIKM '94
This paper investigates a network-based information retrieval model using diagnostic inferencing techniques. A basic inference network in information retrieval consists of two component networks: the document component aud the query component . In our approach, there is a layer of nodes corresponding to the documents, and a layer of nodes corresponding to the index terms extracted from the document set, with links connecting documents to the related index terms 1. A thesaurus is used to
... ide concept categories; these categories are represented by another layer of nodes, with links connecting the index terms and the related categories 2. The query component uses a symmetric structure. Each query causes markings of category nodes, hence markkgs of the related index term nodes, in the document component of the network. In our previous work , we adapted a competition-based connectionist model for diagnostic problem solving  to information retrieval. In this model, documents are treated as '(disorders" and user information needs, represented by the marked index term nodes, as "manifestations". A competitive activation mechanism is then used which converges to a set of disorders that best explain the given manifestations. Our experiments showed that the retrieval performance of this model is comparable to or better than that of various information retrieval models reported in the literature . In this paper, we report further enhancements of the model by usiug a merged thesaurus.