Concept Learning for \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}}\) by Refinement and Reinforcement

  • Mahsa Chitsaz
  • Kewen Wang
  • Michael Blumenstein
  • Guilin Qi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7458)


Ontology construction in OWL is an important and yet time-consuming task even for knowledge engineers and thus a (semi-) automatic approach will greatly assist in constructing ontologies. In this paper, we propose a novel approach to learning concept definitions in \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}} \) from a collection of assertions. Our approach is based on both refinement operator in inductive logic programming and reinforcement learning algorithm. The use of reinforcement learning significantly reduces the search space of candidate concepts. Besides, we present an experimental evaluation of constructing a family ontology. The results show that our approach is competitive with an existing learning system for \(\ensuremath{\cal E}\ensuremath{\cal L}\).


Concept Learning Description Logic \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}}\) Reinforcement Learning Refinement Operator 


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  1. 1.
    Baader, F., Brandt, S., Lutz, C.: Pushing the \(\mathcal{EL}\) Envelope. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 364–369 (2005)Google Scholar
  2. 2.
    Baader, F., Lutz, C., Suntisrivaraporn, B.: Efficient Reasoning in \(\mathcal{EL^{++}}\). In: Proceedings of the 2006 International Workshop on Description Logics, vol. 189 (2006)Google Scholar
  3. 3.
    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
  4. 4.
    Fanizzi, N., Ferilli, S., Iannone, L., Palmisano, I., Semeraro, G.: Downward Refinement in the \(\mathcal{ALN}\) Description Logic. In: Proceedings of the 4th International Conference on Hybrid Intelligent Systems, pp. 68–73. IEEE Computer Society Press (2004)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Kazakov, Y., Krötzsch, M., Simancik, F.: Unchain my \(\mathcal{EL}\) Reasoner. In: Proceedings of the 24th International Workshop on Description Logics. Description Logics, vol. 745 (2011)Google Scholar
  7. 7.
    Lawley, M., Bousquet, C.: Fast Classification in Protege: Snorocket as an OWL 2 EL Reasoner. In: Proceedings of the Australasian Ontology Workshop 2010: Advances in Ontologies, vol. 122, pp. 45–50 (2010)Google Scholar
  8. 8.
    Lehmann, J.: Hybrid Learning of Ontology Classes. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 883–898. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Lehmann, J., Haase, C.: Ideal Downward Refinement in the \(\mathcal{EL}\) Description Logic. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 73–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Lehmann, J., Hitzler, P.: Concept Learning in Description Logics using Refinement Operators. Machine Learning 78(1-2), 203–250 (2010)CrossRefGoogle Scholar
  11. 11.
    Muggleton, S., Feng, C.: Efficient Induction of Logic Programs. In: Proceedings of the New Generation Computing, pp. 368–381 (1990)Google Scholar
  12. 12.
    Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS, vol. 1228. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  13. 13.
    Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A Midterm Report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3–20. Springer, Heidelberg (1993)Google Scholar
  14. 14.
    Rector, A., Horrocks, I.: Experience Building a Large, Re-usable Medical Ontology using a Description Logic with Transitivity and Concept Inclusions. In: Proceedings of the Workshop on Ontological Engineering. AAAI Press (1997)Google Scholar
  15. 15.
    Stearns, M.Q., Price, C., Spackman, K.A., Wang, A.Y.: SNOMED Clinical Terms: Overview of the Development Process and Project Status. In: Proceedings of the Annual Symposium AMIA, pp. 662–666 (2001)Google Scholar
  16. 16.
    The Gene Ontology Consortium: The Gene Ontology Project in 2008. Nucleic Acids Research 36, 440–444 (2008)Google Scholar
  17. 17.
    Watkins, C.: Learning from Delayed Rewards. Ph.D. thesis. University of Cambridge, England (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mahsa Chitsaz
    • 1
    • 2
  • Kewen Wang
    • 1
    • 2
  • Michael Blumenstein
    • 1
    • 2
  • Guilin Qi
    • 1
    • 2
  1. 1.School of Information and Communication TechnologyGriffith UniversityAustralia
  2. 2.School of Computer Science and EngineeringSoutheast UniversityChina

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