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SemioSem: A Semiotic-Based Similarity MeasureXavier Aimé1,3, Frédéric Furst2, Pascale Kuntz1, and Francky Trichet1 1LINA - Laboratoire d’Informatique de Nantes Atlantique (UMR-CNRS 6241), University of Nantes - Team “Knowledge and Decision”, 2 rue de la Houssinière BP 92208 44322 Nantes Cedex 03, France
2MIS - Laboratoire Modélisation, Information et Système, University of Amiens, UPJV, 33 rue Saint Leu 80039 Amiens Cedex 01, France
3Société TENNAXIA, 37 rue de Châteaudun 75009 Paris, France
Abstract. This paper introduces a new similarity measure called SemioSem. The first originality of this measure, which is defined in the context of a semiotic-based approach, is to consider the three dimensions of the conceptualization underlying a domain ontology: the intension (i.e. the properties used to define the concepts), the extension (i.e. the instances of the concepts) and the expression (i.e. the terms used to denote both the concepts and the instances). Thus, SemioSem aims at aggregating and improving existing extensional-based and intensional-based measures, with an original expressional one. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. Indeed, SemioSem is based on multiple informations sources: (1) a textual corpus, validated by the end-user, which must reflect the domain underlying the ontology which is considered, (2) a set of instances known by the end-user, (3) an ontology enriched with the perception of the end-user on how each property associated to a concept c is important for defining c and (4) the emotional state of the end-user. The importance of each source can be modulated according to the context of use and SemioSem remains valid even if one of the source is missing. This makes our measure 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. Keywords: Semantic Measure, Semiotics, Personnalization LNCS 5872, p. 584 ff. lncs@springer.com
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