Wines with geographical indication can be classified and represented by such features as designations of origin, producers, vintage years, alcoholic strength, and grape varieties; these features allow us to define wines in terms of a set of necessary and/or sufficient conditions. However, wines can also be identified by other characteristics, involving their look, smell, and taste; in this case, it is hard to define wines in terms of necessary and/or sufficient conditions, as wine concepts exhibit typicality effects. This is a setback for the design of computer science ontologies aiming to represent wine concepts, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. To solve this problem, we propose to adopt a hybrid approach in which ontology-oriented formalisms are combined with a geometric representation of knowledge based on conceptual spaces. As in conceptual spaces, concepts are identified in terms of a number of quality dimensions. In order to determine those relevant for wine representation, we use the terminology developed by the Italian Association of Sommeliers to describe wines. This will allow us to understand typicality effects about wines, determine prototypes and better exemplars, and measure the degree of similarity between different wines.
Comparison study in statistical estimation of gene effects based on a real data set