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.
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References
Au Yeung, C.M., Leung, H.F.: Ontology with likeliness and typicality of objects in concepts. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 98–111. Springer, Heidelberg (2006)
Blanchard, E., Harzallah, M., Kuntz, P.: A generic framework for comparing semantic similarities on a subsumption hierarchy. In: Proceedings of the 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 20–24. IOS Press, Amsterdam (2008)
Bluck, S., Li, K.: Predicting memory completeness and accuracy: Emotion and exposure in repeated autobiographical recall. Applied Cognitive Psychology (15), 145–158 (2001)
d’Amato, C., Staab, S., Fanizzi, N.: On the influence of description logics ontologies on conceptual similarity. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 48–63. Springer, Heidelberg (2008)
Gabora, L.M., Rosch, E., Aerts, D.: Toward an ecological theory of concepts. Ecological Psychology 20(1-2), 84–116 (2008)
Jaccard, P.: Distribution de la flore alpine dans le bassin des dranses et dans quelques regions voisines. Bulletin de la Societe Vaudoise de Sciences Naturelles 37, 241–272 (1901) (in French)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxinomy. In: International Conference on Research in Computationnal Linguistics, pp. 19–33 (1997)
Leacock, C., Chodorow, M.: Combining local context and Wordnet similarity for word sense identification. In: WordNet: an electronic lexical database, pp. 265–283. MIT Press, Cambridge (1998)
Lin, D.: An information-theoric definition of similarity. In: Proceedings of the 15th international conference on Machine Learning, pp. 296–304 (1998)
Mikulincer, M., Kedem, P., Paz, D.: Anxiety and categorization-1, the structure and boundaries of mental categories. Personality and individual differences 11(8), 805–814 (1990)
Morris, C.W.: Foundations of the Theory of Signs. Chicago University Press (1938)
Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transaction on Systems, Man and Cybernetics 19(1), 17–30 (1989)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research (JAIR) 11, 95–130 (1999)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), vol. 1, pp. 448–453 (1995)
Sanderson, M., Croft, W.B.: Deriving concept hierarchies from text. In: Proceedings of the 22nd International ACM SIGIR Conference, pp. 206–213 (1999)
Sowa, J.: Ontology, metadata, and semiotics. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 55–81. Springer, Heidelberg (2000)
Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)
Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd annual meeting of the Association for Computational Linguistics, pp. 133–138 (1994)
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Aimé, X., Furst, F., Kuntz, P., Trichet, F. (2009). SemioSem: A Semiotic-Based Similarity Measure. In: Meersman, R., Herrero, P., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2009 Workshops. OTM 2009. Lecture Notes in Computer Science, vol 5872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05290-3_72
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DOI: https://doi.org/10.1007/978-3-642-05290-3_72
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