Representing Concepts in Artificial Systems: A Clash of Requirements

The problem of concept representation is relevant for many subfields of cognitive research, including psychology, philosophy and artificial intelligence. In particular, in recent years, it received great attention within knowledge representation, because of its relevance for knowledge engineering and for ontology-based technologies. However, the notion of concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs is that the notion of concept is in some sense heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as, for example, compositionality on the one side and the need of representing prototypical information on the other. AI research in some way shows traces of this situation. In this paper we propose an analysis of this state of affairs. Since it is our opinion that a mature methodology to approach knowledge representation and knowledge engineering should take advantage also from the empirical results of cognitive psychology concerning human abilities, we sketch some proposal for concept representation in formal ontologies, which takes into account suggestions coming from psychological research. Our basic assumption is that knowledge representation technologies designed considering evidences coming from experimental psychology (and, therefore, more similar to the humans way of reasoning and organizing information) can have better results in real life applications (e.g. in the field of Semantic Web).

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