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)


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.


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.


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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

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