ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification

  • Nicola Fanizzi
  • Claudia d’Amato
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)


In order to overcome the limitations of purely deductive approaches to the tasks of classification and retrieval from ontologies, inductive (instance-based) methods have been proposed as efficient and noise-tolerant alternative. In this paper we propose an original method based on non-parametric learning: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided.


Description Logic Training Instance Inductive Procedure Query Answering Semantic Similarity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  2. 2.
    Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Veloso, M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 230–235 (2007)Google Scholar
  3. 3.
    Bloehdorn, S., Sure, Y.: Kernel methods for mining instance data in ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 58–71. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: An inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2001)zbMATHGoogle Scholar
  6. 6.
    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, Bressanone, Italy. CEUR Workshop Proceedings, vol. 250 (2007)Google Scholar
  7. 7.
    Fanizzi, N., d’Amato, C., Esposito, F.: DL-Foil: Concept learning in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS(LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Fanizzi, N., d’Amato, C., Esposito, F.: Statistical learning for inductive query answering on OWL ontologies. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 195–212. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Haase, P., van Harmelen, F., Huang, Z., Stuckenschmidt, H., Sure, Y.: A framework for handling inconsistency in changing ontologies. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 353–367. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Hitzler, P., Vrandečić, D.: Resolution-based approximate reasoning for OWL DL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 383–397. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Huang, Z., van Harmelen, F.: Using semantic distances for reasoning with inconsistent ontologies. In: Sheth, A., et al. (eds.) Proceedings of the 7th International Semantic Web Conference, ISWC 2008. LNCS, vol. 5318, pp. 178–194. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Lukasiewicz, T.: Expressive probabilistic description logics. Artificial Intelligence 172(6-7), 852–883 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Möller, R., Haarslev, V., Wessel, M.: On the scalability of description logic instance retrieval. In: Parsia, B., Sattler, U., Toman, D. (eds.) Proceedings of the 2006 International Workshop on Description Logics, DL 2006. CEUR Workshop Proceedings, vol. 189. CEUR (2006)Google Scholar
  14. 14.
    Tserendorj, T., Rudolph, S., Krötzsch, M., Hitzler, P.: Approximate owl-reasoning with screech. In: Calvanese, D., Lausen, G. (eds.) RR 2008. LNCS, vol. 5341, pp. 165–180. Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Wache, H., Groot, P., Stuckenschmidt, H.: Scalable instance retrieval for the semantic web by approximation. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE 2005 Workshops. LNCS, vol. 3807, pp. 245–254. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Witten, I.H., Frank, E.: Data Mining, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Claudia d’Amato
    • 1
  • Floriana Esposito
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli studi di BariBariItaly

Personalised recommendations