Encoding Commonsense Lexical Knowledge into WordNet

In this paper, we propose an extension of the WordNet conceptual model, with the final purpose of encoding the common sense lexical knowledge associated to words used in everyday life. The extended model has been defined starting from the short descriptions generated by naïve speakers in relation to tar-get concepts (i.e. feature norms). Even if this proposal has been developed primarily for therapeutic purposes, it can be seen as a generalization of the original WordNet model that takes into account a much wider and systematic set of semantic relations. The extended model is also an enhancement of the psycholinguistic vocation of the WordNet model. A featural representation of concepts is nowadays assumed by most models of the human semantic memory. For testing our proposal, we conducted a fea-ture elicitation experiment and collected de-scriptions of 50 concepts from 60 participants. Problematic issues related to the encoding of this information into WordNet are discussed and preliminary results are presented.