TITLE:
Prototypicality Gradient and Similarity Measure: A Semiotic-Based Approach Dedicated to Ontology Personalization
AUTHORS:
X. Aime, F. Furst, P. Kuntz, F. Trichet
KEYWORDS:
Semantic Measure, Conceptual Prototypicality, Lexical Prototypicality, Gradient, Ontology Personalization, Semiotics
JOURNAL NAME:
Intelligent Information Management,
Vol.2 No.2,
March
16,
2010
ABSTRACT: This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.