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

Abstract

In this article, we present an extension of the frame-based language Objlogq , called Ž 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, extension of the inheritance mechanisms, and weighting of structural links. Contrary to other works in the domain, our system is strongly based on a theoretical approach inspired from Zadeh's and Dubois' works. We also defined an original instance classification mechanism, which has the ability to take into account the notions of typicality and similarity as they are presented in the psychological literature. Our model proposes consideration of a particular semantics of default values to estimate the typicality between a class and the instance to classify Ž . ITC . In that way, the possibilities of the typicality representation proposed by framebased languages are exploited. 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 the classification process more flexible. Taking into account additional knowledge concerning heuristics and elements of cognitive psychology leads to the enrichment of the classification mechanism. ᮊ

A Parameterized Representation of Uncertain Conceptual Spaces

“…The most prominent difference between this and other approximate concept modeling approaches (e.g. Faucher 2001, Morris 2003 is the use of an underlying psychological concept space. This makes it possible to use fuzzy arithmetic to measure concept distance and concept inclusion, and the use of rough fuzzy sets, creates an explicit representation of indiscernibility in the concept similarity measures.…”

In Search of Classification that Supports the Dynamics of Science: The FAO Land Cover Classification System and Proposed Modifications

“…It also enables users to compare categories within and across classification terminologies using the standardized descriptive features as a common language. In this way the LCCS is to some extent following one of the criteria of Bowker and Star (1999)`to render voice retrievable' listed above, and it is also well aligned with current cognitive and information theoretic proposals to formally describe and compare categories (see Faucher, 2001;Ga« rdenfors, 2000;Mennis et al, 2000;Tversky, 1977). Conceptually the LCCS system follows a hybrid ontology approach in that it provides a set of characteristics to describe a land-cover class, and it relies on a feature-matching process to evaluate semantic similarity between land-cover classes.…”