Typicality-Based Inference by Plugging Conceptual Spaces Into Ontologies

In this paper we present a cognitively inspired system for the representation of conceptual information in an ontology-based environment. It builds on the heterogeneous notion of concepts in Cognitive Science and on the so-called dual process theories of reasoning and rationality, and it provides a twofold view on the same artificial concept, combining a classical symbolic component (grounded on a formal ontology) with a typicality-based one (grounded on the conceptual spaces framework). The implemented system has been tested in a pilot experimentation regarding the classification task of linguistic stimuli. The results show that this modeling solution extends the representational and reasoning “conceptual” capabilities of standard ontology-based systems.

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