Fuzzy attributes for knowledge representation and acquisition

摘要

Repertory grids and other matrix-like structures can be used to represent knowledge and elicit knowledge from experts. A grid or matrix is a representation of a knowledge domain where the elements in the domain appear long the horizontal axis and constructs or attributes of the elements appear along the horizontal axis and constructs or attributes of the elements appear along the vertical axis. Each construct is rated for its presence in a given element or how much a construct applies to an element. Analysis of these ratings can determine similarities and differences between the elements. Traditionally, constructs are bipolar entities where a rating falls on a range from one pole to the other. For example, temperature may be represented by the bipolar construct hot-cold and a range of 1 to 5 in which 1 represents hot and 5 represents cold. Ratings of 2, 3, and 4 lie in between hot and cold. Additionally, all constructs in a grid have the same range of values and the range in arbitrarily chosen. This paper presents a method for translating grid ratings in to fuzzy membership values. The fuzzy membership values become the values for describing and analyzing the associations between elements. Thus, constructs no longer need to use the same scaling range and no longer need to be bipolar. A construct of an element now becomes a true attribute of an element. An attribute can be rated in its own range and with its own unit of measurement. In the previous example, the bipolar construct hot-cold becomes simply, temperature measured in degrees. Experts or users need no longer translate to an artificial rating range.