Conceptual spaces as a framework for knowledge representation

The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a varietyof non-sy mbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent theylack a higher-level theoryof representation. In this paper, conceptual spaces are suggested as an appropriate framework for non-symbolic models. Conceptual spaces consist of a number of “qualitydimensions” that often are derived from perceptual mechanisms. It will be outlined how conceptual spaces can represent various kind of information and how theycan be used to describe concept learning. The connections to prototype theory will also be presented.