Advertisement

Kernel Methods for Mining Instance Data in Ontologies

  • Stephan Bloehdorn
  • York Sure
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4825)

Abstract

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

Keywords

Support Vector Machine Description Logic Machine Learning Algorithm Object Property Kernel Method 
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.

References

  1. 1.
    Brickley, D., Guha, R.: RDF vocabulary description language 1.0: RDF schema. W3C Recommendation, 10 February 2004, Published online (2004), at http://www.w3.org/TR/2004/REC-rdf-schema-20040210/
  2. 2.
    McGuinness, D.L., van Harmelen, F.: OWL web ontology language overview. W3C Recommendation, 10 February 2004, Published online (2004), at http://www.w3.org/TR/2004/REC-owl-features-20040210/
  3. 3.
    Buitelaar, P., Cimiano, P., Magnini, B.: Trends in Information Processing Systems. Frontiers in Artificial Intelligence, vol. 123, p. 180. IOS Press, Amsterdam (2005)Google Scholar
  4. 4.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis (Hardcover). Cambridge University Press, Cambridge (2004)Google Scholar
  5. 5.
    Staab, S., Studer, R.: Handbook on Ontologies. Springer, Aix-en-Provence, France (2003)Google Scholar
  6. 6.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  7. 7.
    Vapnik, V., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation and signal processing. In: NIPS, pp. 281–287 (1996)Google Scholar
  8. 8.
    Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Technical Report 44, Max Planck Institute for Biological Cybernetics, Tübingen, Germany (1996)Google Scholar
  9. 9.
    Muggleton, S.H., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19,20, 629–679 (1994)CrossRefGoogle Scholar
  10. 10.
    Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations 5(1), 49–58 (2003)CrossRefGoogle Scholar
  11. 11.
    Haussler, D.: Convolution kernels on discrete structures. Technical Report Technical Report UCS-CRL-99-10, UC Santa Cruz (1999)Google Scholar
  12. 12.
    Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and distances for structured data. Machine Learning 57(3), 205–232 (2004)CrossRefzbMATHGoogle Scholar
  13. 13.
    Raedt, L.D., Passerini, A.: Kernels on prolog proof trees: Statistical learning in the ILP setting. Journal of Machine Learning Research 7, 307–342 (2006)Google Scholar
  14. 14.
    Ehrig, M., Haase, P., Stojanovic, N., Hefke, M.: Similarity for ontologies - a comprehensive framework. In: ECIS 2005. Proceedings of the 13th European Conference on Information Systems, Regensburg, Germany, May 26-28, 2005 (2005)Google Scholar
  15. 15.
    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. Journal of Machine Learning Research 2, 419–444 (2002)CrossRefzbMATHGoogle Scholar
  16. 16.
    Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)Google Scholar
  17. 17.
    Sure, Y., Bloehdorn, S., Haase, P., Hartmann, J., Oberle, D.: The SWRC ontology - semantic web for research communities. In: Jajodia, S., Mazumdar, C. (eds.) ICISS 2005. LNCS, vol. 3803, pp. 218–231. Springer, Heidelberg (2005)Google Scholar
  18. 18.
    Krogel, M.-A., Rawles, S., Zelezny, F., Flach, P., Lavrac, N., Wrobel, S.: Comparative evaluation of approaches to propositionalization. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 194–217. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Cristianini, N., Shawe-Taylor, J.: On kernel-target alignment. In: Advances in Neural Information Processing Systems 14 - Proceedings of NIPS 2001, Vancouver, Canada, December 3-8, 2001, pp. 367–373 (2001)Google Scholar
  20. 20.
    Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: ICML 2002. Proceedings of the Nineteenth International Conference on Machine Learning, pp. 315–322. Morgan Kaufmann, San Francisco (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Stephan Bloehdorn
    • 1
  • York Sure
    • 2
  1. 1.Institute AIFB, University of Karlsruhe, D-76128 KarlsruheGermany
  2. 2.SAP AG, Research Center CEC Karlsruhe, Vincenz-Prießnitz-Str. 1, D-76131 KarlsruheGermany

Personalised recommendations