Abstract
In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology environment. Before the measure model is adopted, an ontology is required to be converted into a lightweight ontology space, and within it all the ontology concepts need to be transformed into the pseudo-concepts. By means of this model, ontology concept similarities are measured respectively based on the content of pseudo-concepts and the structure of the lightweight ontology space. Afterwards, the two aspects of concept similarity are leveraged as the eventual product. In addition, an experiment is conducted to evaluate the measure model based on a small ontology. Conclusions are drawn and future works are planned in the final section.
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References
Pedersen, T., Pakhomov, S.V.S., Patwardhan, S., Chute, C.G.: Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics 40, 288–299 (2006)
Miller, G., Charles., W.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6, 1–28 (1991)
Rubenstein, H., Goodenough, J.B.: Contextual Correlates of Synonymy. Communications of the ACM 8, 627–633 (1965)
Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and Application of a Metric on Semantic Nets. IEEE Transactions on Systems, Man and Cybernetics 19, 17–30 (1989)
Srihari, R.K., Zhang, Z.F., Rao, A.B.: Intelligent indexing and semantic retrieval of multimodal documents. Information Retrieval 2, 245–275 (2000)
Sussna, M.: Word Sense Disambiguation for Free-text Indexing Using a Massive Semantic Network. In: The Second International Conference on Information and Knowledge Management (CIKM 1993), pp. 67–74. ACM, Washington (1993)
Li, Y., Bandar, Z.A., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Transactions on Knowledge and Data Engineering 15, 871–882 (2003)
Lin, D.: Automatic retrieval and clustering of similar words. In: the 17th COLING, pp. 768–774. ACM, Austin (1998)
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 11, 95–130 (1999)
Rosenfield, R.: A maximum entropy approach to adaptive statistical modelling. Computer Speech and Language 10, 187–228 (1996)
Steichen, O., Bozec, C.D., Thieu, M., Zapletal, E., Jaulent, M.C.: Computation of semantic similarity within an ontology of breast pathology to assist inter-observer consensus. Computers in Biology and Medicine 36, 768–788 (2006)
Othman, R.M., Deris, S., Illias, R.M.: A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences. Journal of Biomedical Informatics 41, 65–81 (2008)
Sevilla, J.L., Segura, V.c., Podhorski, A., Guruceaga, E., Mato, J.M., MartÃnez-Cruz, L.A., Corrales, F.J., Rubio, A.: Correlation between Gene Expression and GO Semantic Similarity. IEEE/ACM Transaction on Computational Biology and Bioinformatics 2, 330–338 (2005)
Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1995)
Sowa, J.F.: Semantic Networks. In: Shapiro, S.C. (ed.) Encyclopedia of Artificial Intelligence. Wiley, Chichester (1992)
Costello, R.L.: OWL ontologies (2009)
Kuropka, D.: Modelle zur repräsentation natürlichsprachlicher dokumente. ontologie-basiertes information-filtering und -retrieval mit relationalen datenbanken. In: Becker, J., Grob, H.L., Klein, S., Kuchen, H., Müller-Funk, U., Vossen, G. (eds.) Advances in Information Systems and Management Science. Logos Verlag Berlin, Berlin (2004)
Furnas, G.W., Deerwester, S., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular decomposition model of latent semantic structure. In: 11th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 465–480 (1988)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Harlow (1999)
Dong, H., Hussain, F.K., Chang, E.: A concept similarity measure model for enhancing the dependability of semantic service matchmaking in the service ecosystem. IEEE Transactions on Service Computing (submitted, 2009)
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Dong, H., Hussain, F.K., Chang, E. (2009). A Hybrid Concept Similarity Measure Model for Ontology Environment. 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_103
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DOI: https://doi.org/10.1007/978-3-642-05290-3_103
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