Tractable Feature Generation Through Description Logics with Value and Number Restrictions

  • Nicola Fanizzi
  • Luigi Iannone
  • Nicola Di Mauro
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


In the line of a feature generation paradigm based on relational concept descriptions, we extend the applicability to other languages of the Description Logics family endowed with specific language constructors that do not have a counterpart in the standard relational representations, such as clausal logics. We show that the adoption of an enhanced language does not increase the complexity of feature generation, since the process is still tractable. Moreover this can be considered as a formalization for future employment of even more expressive languages from the Description Logics family.


Description Logic Expressive Language Target Concept Concept Description Concept Graph 
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  1. 1.
    Valiant, L.G.: Robust logics. In: Proceedings of the 31st Annual ACM Symposium on the Theory of Computing, pp. 642–651 (1999)Google Scholar
  2. 2.
    Kramer, S., Lavrač, N., Džeroski, S.: Propositionalization approaches to relational data mining. In: Džeroski, S., Lavrač, N. (eds.) Relational Data Mining. Springer, Heidelberg (2001)Google Scholar
  3. 3.
    Cumby, C.M., Roth, D.: Learning with feature description logics. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 32–47. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  5. 5.
    Blum, A.: Learning boolean functions in an infinite attribute space. Machine Learning 9, 373–386 (1992)zbMATHGoogle Scholar
  6. 6.
    Calvanese, D., Lenzerini, M., Nardi, D.: Unifying class-based representation formalisms. Journal of Artificial Intelligence Research 11, 199–240 (1999)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Molitor, R.: Structural subsumption for ALN. Technical Report LTCS-98-03, LuFg Theoretical Computer Science, RWTH Aachen, Germany (1998)Google Scholar
  8. 8.
    Baader, F., Küsters, R.: Computing the least common subsumer and the most specific concept in the presence of cyclic ALN concept descriptions. In: Herzog, O. (ed.) KI 1998. LNCS, vol. 1504, pp. 129–140. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Donini, F.M., Lenzerini, M., Nardi, D., Nutt, W.: The complexity of concept languages. Information and Computation 134, 1–58 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Küsters, R., Molitor, R.: Approximating most specific concepts in description logics with existential restrictions. In: Baader, F., Brewka, G., Eiter, T. (eds.) KI 2001. LNCS (LNAI), vol. 2174, pp. 33–47. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Carleson, A., Cumby, C., Rosen, J., Roth, D.: The SNoW learning architecture. Technical Report UIUCDCS-R-99-2101, CS Dept., University of Illinois at Urbana-Champaign (1999)Google Scholar
  12. 12.
    Mantay, T.: Commonality-based ABox retrieval. Technical Report FBI-HH-M-291/2000, Department of Computer Science, University of Hamburg, Germany (2000)Google Scholar
  13. 13.
    Cohen, W.W., Borgida, A., Hirsh, H.: Computing least common subsumers in description logic. In: Proceedings of the 10th National Conference on Artificial Intelligence, AAAI 1992. MIT Press, Cambridge (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Luigi Iannone
    • 1
  • Nicola Di Mauro
    • 1
  • Floriana Esposito
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

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