A formal model of diagnostic inference. I. Problem formulation and decomposition
Information Sciences: an International Journal - Special issue on expert systems
Databases: a primer for retrieving information by computer
Databases: a primer for retrieving information by computer
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Merging Thesauri: Principles and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A neural algorithm for document clustering
Information Processing and Management: an International Journal - Special issue on parallel processing and information retrieval
Overview of the first TREC conference
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Computation of term associations by a neural network
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic thesaurus discovery via selective natural language processing: a corpus based approach
Automatic thesaurus discovery via selective natural language processing: a corpus based approach
Computer-based linking of thesauri
Computer-based linking of thesauri
Incorporating latent semantic indexing into a neural network model for information retrieval
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
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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 and 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 provide 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 markings 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 using a merged thesaurus.