Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Extended Semantic Web Conference

ESWC 2012: The Semantic Web: Research and Applications pp 134–148Cite as

  1. Home
  2. The Semantic Web: Research and Applications
  3. Conference paper
Graph Kernels for RDF Data

Graph Kernels for RDF Data

  • Uta Lösch21,
  • Stephan Bloehdorn22 &
  • Achim Rettinger21 
  • Conference paper
  • 3408 Accesses

  • 51 Citations

  • 3 Altmetric

Part of the Lecture Notes in Computer Science book series (LNISA,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.

Download conference paper PDF

References

  1. Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)

    CrossRef  Google Scholar 

  2. Augenstein, I., Padó, S., Rudolph, S.: Lodifier: Generating Linked Data from Unstructured Text. In: Simperl, E., et al. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 210–224. Springer, Heidelberg (2012)

    Google Scholar 

  3. Brickley, D., Miller, L.: FOAF vocabulary specification. Technical report, FOAF project (2007), http://xmlns.com/foaf/spec/20070524.html (Published online on May 24, 2007)

  4. Fanizzi, N., d’Amato, C.: A Declarative Kernel for \(\mathcal{ALC}\) Concept Descriptions. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 322–331. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  5. Fanizzi, N., d’Amato, C., Esposito, F.: Statistical Learning for Inductive Query Answering on OWL Ontologies. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 195–212. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  6. Bloehdorn, S., Sure, Y.: Kernel Methods for Mining Instance Data in Ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 58–71. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  7. Rettinger, A., Nickles, M., Tresp, V.: Statistical Relational Learning with Formal Ontologies. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 286–301. Springer, Heidelberg (2009)

    Google Scholar 

  8. Huang, Y., Tresp, V., Bundschus, M., Rettinger, A., Kriegel, H.-P.: Multivariate Prediction for Learning on the Semantic Web. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS, vol. 6489, pp. 92–104. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  9. Bicer, V., Tran, T., Gossen, A.: Relational Kernel Machines for Learning from Graph-Structured RDF Data. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 47–62. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  10. Thor, A., Anderson, P., Raschid, L., Navlakha, S., Saha, B., Khuller, S., Zhang, X.-N.: Link Prediction for Annotation Graphs Using Graph Summarization. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 714–729. Springer, Heidelberg (2011)

    Google Scholar 

  11. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)

    Google Scholar 

  12. Getoor, L., Friedman, N., Koller, D., Pferrer, A., Taskar, B.: Probabilistic relational models. In: Introduction to Statistical Relational Learning. MIT Press (2007)

    Google Scholar 

  13. Haussler, D.: Convolution kernels on discrete structures. Technical Report UCS-CRL-99-10, University of California at Santa Cruz (1999)

    Google Scholar 

  14. Gärtner, T., Flach, P., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 129–143. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  15. Horváth, T., Gärtner, T., Wrobel, S.: Cyclic pattern kernels for predictive graph mining. In: Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2004), pp. 158–167. ACM Press, New York (2004)

    CrossRef  Google Scholar 

  16. Shervashidze, N., Borgwardt, K.: Fast subtree kernels on graphs. In: Advances in Neural Information Processing Systems, vol. 22 (2009)

    Google Scholar 

  17. Güting, R.H.: Datenstrukturen und Algorithmen. B.G. Teubner, Stuttgart (1992)

    Google Scholar 

  18. Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods - Support Vector Learning (1999)

    Google Scholar 

  19. Chang, C.C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  20. Sure, Y., Bloehdorn, S., Haase, P., Hartmann, J., Oberle, D.: The SWRC Ontology – Semantic Web for Research Communities. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, pp. 218–231. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  21. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Technical report (1999)

    Google Scholar 

  22. Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proc. of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 41–48. ACM (2000)

    Google Scholar 

  23. Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: Proc. of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2004), pp. 25–32. ACM (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany

    Uta Lösch & Achim Rettinger

  2. IBM Germany, 12277, Berlin, Germany

    Stephan Bloehdorn

Authors
  1. Uta Lösch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Bloehdorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Achim Rettinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute AIFB, Karlsruhe Institute of Technology, Englerstrasse 11, 76131, Karlsruhe, Germany

    Elena Simperl

  2. CITEC, University of Bielefeld, Morgenbreede 39, 33615, Bielefeld, Germany

    Philipp Cimiano

  3. Siemens AG Österreich, Siemensstrasse 90, 1210, Vienna, Austria

    Axel Polleres

  4. Technical University of Madrid, C/ Severo Ochoa, 13, 28660, Boadilla del Monte, Madrid, Spain

    Oscar Corcho

  5. STLab, ISTC-CNR, Via Nomentana 56, 00161, Rome, Italy

    Valentina Presutti

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lösch, U., Bloehdorn, S., Rettinger, A. (2012). Graph Kernels for RDF Data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_16

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-30284-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30283-1

  • Online ISBN: 978-3-642-30284-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature