Graph Kernels for RDF Data

  • Uta Lösch
  • Stephan Bloehdorn
  • Achim Rettinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

The increasing availability of structured data in (RDF) format poses new challenges and opportunities for data mining. Existing approaches to mining RDF have only focused on one specific data representation, one specific machine learning algorithm or one specific task. Kernels, however, promise a more flexible approach by providing a powerful framework for decoupling the data representation from the learning task. This paper focuses on how the well established family of kernel-based machine learning algorithms can be readily applied to instances represented as RDF graphs. We first review the problems that arise when conventional graph kernels are used for RDF graphs. We then introduce two versatile families of graph kernels specifically suited for RDF, based on intersection graphs and intersection trees. The flexibility of the approach is demonstrated on two common relational learning tasks: entity classification and link prediction. The results show that our novel RDF graph kernels used with (SVMs) achieve competitive predictive performance when compared to specialized techniques for both tasks.

Keywords

Kernel Function Intersection Graph Intersection Tree Link Prediction Link Open Data 
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 2012

Authors and Affiliations

  • Uta Lösch
    • 1
  • Stephan Bloehdorn
    • 2
  • Achim Rettinger
    • 1
  1. 1.Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.IBM GermanyBerlinGermany

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