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Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies

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Database and Expert Systems Applications (DEXA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5181))

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Abstract

We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). In the algorithm, the possible clusterings are represented as strings of central elements (medoids, w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter; the method is able to find an optimal choice by means of the evolutionary operators and of a fitness function. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.

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References

  1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  2. Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics 28(3), 301–315 (1998)

    Article  Google Scholar 

  3. 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 Proc., vol. 147 (2005)

    Google Scholar 

  4. d’Amato, C., Fanizzi, N., Esposito, F.: A dissimilarity measure for \(\cal ALC\) concept descriptions. In: Proceedings of the 21st Annual ACM Symposium of Applied Computing, SAC2006, Dijon, France, vol. 2, pp. 1695–1699. ACM, New York (2006)

    Google Scholar 

  5. d’Amato, C., Fanizzi, N., Esposito, F.: Reasoning by analogy in description logics through instance-based learning. In: Tummarello, G., et al. (eds.) Proc. of Workshop on Semantic Web Applications and Perspectives, SWAP 2006. CEUR, vol. 201 (2006)

    Google Scholar 

  6. d’Amato, C., Staab, S., Fanizzi, N., Esposito, F.: Efficient discovery of services specified in description logics languages. In: Proc. of the ISWC Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (2007)

    Google Scholar 

  7. d’Aquin, M., Lieber, J., Napoli, A.: Decentralized case-based reasoning for the Semantic Web. In: Gill, Y., et al. (eds.) Proc. of the 4th Int. Semantic Web Conf., ISWC 2005. LNCS, vol. 3279, pp. 142–155. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Knowledge-intensive induction of terminologies from metadata. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 441–455. Springer, Heidelberg (2004)

    Google Scholar 

  9. Esposito, F., Ferilli, S., Fanizzi, N., Basile, T.M.A., Di Mauro, N.: Incremental learning and concept drift in INTHELEX. Jour. of Intelligent Data Analysis 8(1/2), 133–156 (2004)

    Google Scholar 

  10. Fanizzi, N., d’Amato, C., Esposito, F.: Induction of optimal semi-distances for individuals based on feature sets. In: Calvanese, D., et al. (eds.) Working Notes of the 20th International Description Logics Workshop, DL 2007. CEUR, vol. 250 (2007)

    Google Scholar 

  11. Fanizzi, N., d’Amato, C., Esposito, F.: Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases. In: Silva, M.J., et al. (eds.) Proc. of the 16th ACM Conf. on Information and Knowledge Management, pp. 51–60. ACM, New York (2007)

    Google Scholar 

  12. 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. 99–110. Springer, Heidelberg (2004)

    Google Scholar 

  13. Ghozeil, A., Fogel, D.B.: Discovering patterns in spatial data using evolutionary programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1996: Proc. of the 1st Annual Conf., pp. 521–527. MIT Press, Cambridge (1996)

    Google Scholar 

  14. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17(2-3), 107–145 (2001)

    Article  MATH  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  17. Janowicz, K.: Sim-dl: Towards a semantic similarity measurement theory for the description logic \(\mathcal{ALCNR}\) in geographic information retrieval. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1681–1692. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, Chichester (1990)

    Google Scholar 

  19. Kietz, J.-U., Morik, K.: A polynomial approach to the constructive induction of structural knowledge. Machine Learning 14(2), 193–218 (1994)

    Article  MATH  Google Scholar 

  20. Lee, C.-Y., Antonsson, E.K.: Variable length genomes for evolutionary algorithms. In: Whitley, L., et al. (eds.) Proc. of the Genetic and Evolutionary Computation Conference, GECCO 2000, p. 806. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  21. Lehmann, J.: Concept learning in description logics. Master’s thesis, Dresden University of Technology (2006)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. Spinosa, E.J., de Leon, A.P., de Carvalho, F., Gama, J.: OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams. In: Proc. of the Annual ACM Symposium of Applied Computing, vol. 1, pp. 448–452. ACM, New York (2007)

    Google Scholar 

  24. Stepp, R.E., Michalski, R.S.: Conceptual clustering of structured objects: A goal-oriented approach. Artificial Intelligence 28(1), 43–69 (1986)

    Article  Google Scholar 

  25. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)

    Google Scholar 

  26. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search – The Metric Space Approach. Advances in database Systems. Springer, Heidelberg (2007)

    Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, Campus Universitario, Via Orabona 4, 70125, Bari, Italy

    Nicola Fanizzi, Claudia d’Amato & Floriana Esposito

Authors
  1. Nicola Fanizzi
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  2. Claudia d’Amato
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  3. Floriana Esposito
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Sourav S. Bhowmick Josef Küng Roland Wagner

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Fanizzi, N., d’Amato, C., Esposito, F. (2008). Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_73

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  • DOI: https://doi.org/10.1007/978-3-540-85654-2_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85653-5

  • Online ISBN: 978-3-540-85654-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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