Approximate knowledge modeling and classification in a frame-based language: The system CAIN

In this article, we present an extension of the frame-based language Objlog +, called CAIN, which allows the homogeneous representation of approximate knowledge (fuzzy, uncertain, and default knowledge) by means of new facets. We developed elements to manage approximate knowledge: fuzzy operators, ex tension of the inheritance mechanisms, and weighting of structural links. C ontrary to other works in the domain, our system is strongly based on a the oretical approach inspired from Zadeh's and Dubois' works. We also defined an original instance classification mechanism, which has the ability to tak e into account the notions of typicality and similarity as they are present ed in the psychological literature. Our model proposes consideration of a p articular semantics of default values to estimate the typicality between a class and the instance to classify (ITC). In that way, the possibilities of the typicality representation proposed by frame-based languages are exploi ted. To find the most appropriate solution we do not systematically choose the most specific class that matches the ITC but we retain the most typical solution. Approximate knowledge is used to make the matching used during t he classification process more flexible. Taking into account additional kno wledge concerning heuristics and elements of cognitive psychology leads to the enrichment of the classification mechanism. (C) 2001 John Wiley & Sons, Inc.