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.
This work was funded by the X-Media project (www.x-media-project.org) sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant number IST-FP6-026978.
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References
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/
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/
Buitelaar, P., Cimiano, P., Magnini, B.: Trends in Information Processing Systems. Frontiers in Artificial Intelligence, vol. 123, p. 180. IOS Press, Amsterdam (2005)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis (Hardcover). Cambridge University Press, Cambridge (2004)
Staab, S., Studer, R.: Handbook on Ontologies. Springer, Aix-en-Provence, France (2003)
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)
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)
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)
Muggleton, S.H., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19,20, 629–679 (1994)
Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations 5(1), 49–58 (2003)
Haussler, D.: Convolution kernels on discrete structures. Technical Report Technical Report UCS-CRL-99-10, UC Santa Cruz (1999)
Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and distances for structured data. Machine Learning 57(3), 205–232 (2004)
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)
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)
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)
Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)
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)
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)
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)
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)
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Bloehdorn, S., Sure, Y. (2007). Kernel Methods for Mining Instance Data in Ontologies. In: Aberer, K., et al. The Semantic Web. ISWC ASWC 2007 2007. Lecture Notes in Computer Science, vol 4825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76298-0_5
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DOI: https://doi.org/10.1007/978-3-540-76298-0_5
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