Adapting a diagnostic problem-solving model to information retrieval

In this paper, a competition-based connectionist model for diagnostic problem-solving is adapted to information retrieval. 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 di culties 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

• Type: Journal
• Year: 2000
• Where: IPM
• Authors: Inien Syu, Sheau-Dong Lang
• Keywords: Bayesian Inference, Competition-based Connectionist Model, IPM 2000, User Information