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Representing wine concepts: A hybrid approach
- Source :
- Applied Ontology. 15:475-491
- Publication Year :
- 2020
- Publisher :
- IOS Press, 2020.
-
Abstract
- 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.
- Subjects :
- Wine
Linguistics and Language
General Computer Science
Computer science
business.industry
Representation of concepts, Formal ontologies, Conceptual spaces, Wine classification, Wine concepts
Wine classification
02 engineering and technology
Machine learning
computer.software_genre
Hybrid approach
Language and Linguistics
03 medical and health sciences
0302 clinical medicine
Formal ontologies
Wine concepts
0202 electrical engineering, electronic engineering, information engineering
Conceptual spaces
020201 artificial intelligence & image processing
Representation of concepts
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 18758533 and 15705838
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Applied Ontology
- Accession number :
- edsair.doi.dedup.....48b93b03a3354ea633880f86847db199