A Declarative Kernel for \(\mathcal{ALC}\) Concept Descriptions

  • Nicola Fanizzi
  • Claudia d’Amato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This work investigates on kernels that are applicable to semantic annotations expressed in Description Logics which are the theoretical counterpart of the standard representations for the Semantic Web. Namely, the focus is on the definition of a kernel for the \(\mathcal{ALC}\) logic, based both on the syntax and on the semantics of concept descriptions. The kernel is proved to be valid. Furthermore, semantic distance measures are induced from the kernel function.


Normal Form Kernel Function Description Logic Kernel Method Convolution Kernel 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284, 34–43 (2001)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)MATHGoogle 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.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  5. 5.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  6. 6.
    Gärtner, T., Lloyd, J., Flach, P.: Kernels and distances for structured data. Machine Learning 57, 205–232 (2004)MATHCrossRefGoogle Scholar
  7. 7.
    Passerini, A., Frasconi, P., Raedt, L.D.: Kernels on prolog proof trees: Statistical learning in the ILP setting. Journal of Machine Learning Research 7, 307–342 (2006)Google Scholar
  8. 8.
    Haussler, D.: Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, Department of Computer Science, University of California – Santa Cruz (1999)Google Scholar
  9. 9.
    Khardon, R., Roth, D., Servedio, R.: Efficiency versus convergence of boolean kernels for on-line learning algorithms. MIT Press, Cambridge (2002)Google Scholar
  10. 10.
    Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations 5, 49–58 (2003)CrossRefGoogle Scholar
  11. 11.
    Cumby, C., Roth, D.: On kernel methods for relational learning. In: Fawcett, T., Mishra, N. (eds.) Proceedings of the 20th International Conference on Machine Learning, ICML 2003, pp. 107–114. AAAI Press, Menlo Park (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Claudia d’Amato
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

Personalised recommendations