Abstract
A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discover emerging groupings of resources expressed in the standard ontology languages. The method exploits a language-independent semi-distance measure over the space of resources, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. A maximally discriminating group of features can be constructed through a feature construction method based on genetic programming. The evolutionary clustering algorithm proposed is based on the notion of medoids applied to relational representations. It is able to induce a set of clusters by means of a fitness function based on a discernibility criterion. An experimentation with some ontologies proves the feasibility of our method.
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References
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (May 2001)
Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics 28(3), 301–315 (1998)
Borgida, A., Walsh, T.J., Hirsh, H.: Towards measuring similarity in description logics. In: Horrocks, I., Sattler, U., Wolter, F. (eds.) Working Notes of the International Description Logics Workshop, Edinburgh, UK, CEUR Workshop Proceedings, vol. 147 (2005)
d’Amato, C., Fanizzi, N., Esposito, F.: Reasoning by analogy in description logics through instance-based learning. In: Tummarello, G., Bouquet, P., Signore, O. (eds.) Proceedings of Semantic Web Applications and Perspectives, 3rd Italian Semantic Web Workshop, SWAP 2006, Pisa, Italy, CEUR Workshop Proceedings, vol. 201 (2006)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases. In: Proceedings of the 2nd Conference of ACM SIGKDD, pp. 226–231 (1996)
Fanizzi, N., d’Amato, C., Esposito, F.: A hierarchical clustering procedure for semantically annotated resources. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 266–277. Springer, Heidelberg (2007)
Fanizzi, N., d’Amato, C., Esposito, F.: Induction of optimal semi-distances for individuals based on feature sets. In: Working Notes of the International Description Logics Workshop, DL 2007, Bressanone, Italy, CEUR Workshop Proceedings, vol. 250 (2007)
Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Concept formation in expressive description logics. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 99–113. Springer, Heidelberg (2004)
Ghozeil, A., Fogel, D.B.: Discovering patterns in spatial data using evolutionary programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 521–527. MIT Press, Cambridge (1996)
Hall, L.O., Özyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evolutionary Computation 3(2), 103–112 (1999)
Hirano, S., Tsumoto, S.: An indiscernibility-based clustering method. In: Hu, X., Liu, Q., Skowron, A., Lin, T.Y., Yager, R., Zhang, B. (eds.) 2005 IEEE International Conference on Granular Computing, pp. 468–473. IEEE, Los Alamitos (2005)
Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159 (2007)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)
Kietz, J.-U., Morik, K.: A polynomial approach to the constructive induction of structural knowledge. Machine Learning 14(2), 193–218 (1994)
Lee, C.-Y., Antonsson, E.K.: Variable length genomes for evolutionary algorithms. In: Whitley, L., Goldberg, D., Cantú-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2000, p. 806. Morgan Kaufmann, San Francisco (2000)
Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the ALC description logic. In: ILP 2007. LNCS, vol. 4894, pp. 147–160. Springer, Heidelberg (2007)
Nasraoui, O., Krishnapuram, R.: One step evolutionary mining of context sensitive associations and web navigation patterns. In: Proceedings of the SIAM conference on Data Mining, Arlington, VA, pp. 531–547 (2002)
Ng, R., Han, J.: Efficient and effective clustering method for spatial data mining. In: Proceedings of the 20th Conference on Very Large Databases, VLDB 1994, pp. 144–155 (1994)
Nienhuys-Cheng, S.-H.: Distances and limits on herbrand interpretations. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 250–260. Springer, Heidelberg (1998)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Schickel-Zuber, V., Faltings, B.: OSS: A semantic similarity function based on hierarchical ontologies. In: Veloso, M.M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI 2007, Hyderabad, India, pp. 551–556 (2007)
Sebag, M.: Distance induction in first order logic. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 264–272. Springer, Heidelberg (1997)
Stepp, R.E., Michalski, R.S.: Conceptual clustering of structured objects: A goal-oriented approach. Artificial Intelligence 28(1), 43–69 (1986)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer, Heidelberg (2007)
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Esposito, F., Fanizzi, N., d’Amato, C. (2008). Conceptual Clustering Applied to Ontologies. In: Raś, Z.W., Tsumoto, S., Zighed, D. (eds) Mining Complex Data. MCD 2007. Lecture Notes in Computer Science(), vol 4944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68416-9_4
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