Issue No. 01 - February (1995 vol. 7)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.368514
<p><it>Abstract</it>—In this paper, we propose a model for structuring knowledge in vague and continuous domains where similarity plays a role in coming up with plausible inferences. The model consists of two levels, one of which is an inference network with nodes representing concepts and links representing rules connecting concepts, and the other is a microfeature-based replica of the first level. Based on the interaction between the concept nodes and microfeature nodes in the model, inferences are facilitated and knowledge not explicitly encoded in a system can be deduced via mixed similarity matching and rule application. The model is able to take account of many important desiderata of plausible reasoning and produces sensible conclusions accordingly. Examples will be presented to illustrate the utility of the model in structuring knowledge to enable useful inferences to be carried out in several domains.</p>
Artificial intelligence, knowledge-based systems, neural networks, reasoning, knowledge representation, vagueness.
R. Sun, "Structuring Knowledge In Vague Domains," in IEEE Transactions on Knowledge & Data Engineering, vol. 7, no. , pp. 120-136, 1995.