Multivariate Prediction for Learning on the Semantic Web

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6489)


One of the main characteristics of Semantic Web (SW) data is that it is notoriously incomplete: in the same domain a great deal might be known for some entities and almost nothing might be known for others. A popular example is the well known friend-of-a-friend data set where some members document exhaustive private and social information whereas, for privacy concerns and other reasons, almost nothing is known for other members. Although deductive reasoning can be used to complement factual knowledge based on the ontological background, still a tremendous number of potentially true statements remain to be uncovered. The paper is focused on the prediction of potential relationships and attributes by exploiting regularities in the data using statistical relational learning algorithms. We argue that multivariate prediction approaches are most suitable for dealing with the resulting high-dimensional sparse data matrix. Within the statistical framework, the approach scales up to large domains and is able to deal with highly sparse relationship data. A major goal of the presented work is to formulate an inductive learning approach that can be used by people with little machine learning background. We present experimental results using a friend-of-a-friend data set.


Singular Value Decomposition Resource Description Framework Statistical Unit Latent Dirichlet Allocation 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Raedt, L.D., Jaeger, M., Lee, S.D., Mannila, H.: A theory of inductive query answering. In: ICDM (2002)Google Scholar
  2. 2.
    Kiefer, C., Bernstein, A., Locher, A.: Adding data mining support to SPARQL via statistical relational learning methods. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 478–492. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Getoor, L., Friedman, N., Koller, D., Pferrer, A., Taskar, B.: Probabilistic relational models. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)Google Scholar
  4. 4.
    Domingos, P., Richardson, M.: Markov logic: A unifying framework for statistical relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)Google Scholar
  5. 5.
    Xu, Z., Tresp, V., Yu, K., Kriegel, H.P.: Infinite hidden relational models. In: Uncertainty in Artificial Intelligence (UAI) (2006)Google Scholar
  6. 6.
    Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: Poceedings of the National Conference on Artificial Intelligence, AAAI (2006)Google Scholar
  7. 7.
    Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5(3) (1990)Google Scholar
  8. 8.
    Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Proceedings of the 1st Conference on Algorithmic Learning Theory, Ohmsma, Tokyo (1990)Google Scholar
  9. 9.
    De Raedt, L.: Attribute-value learning versus inductive logic programming: The missing links (extended abstract). In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Lavrač, N., Džeroski, S., Grobelnik, M.: Learning nonrecursive definitions of relations with LINUS. In: EWSL 1991: Proceedings of the European Working Session on Learning on Machine Learning (1991)Google Scholar
  11. 11.
    Tresp, V., Yu, K.: Learning with dependencies between several response variables. In: Tutorial at ICML (2009)Google Scholar
  12. 12.
    Tresp, V., Huang, Y., Bundschus, M., Rettinger, A.: Materializing and querying learned knowledge. In: Proceedings of the First ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web (2009)Google Scholar
  13. 13.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature (1999)Google Scholar
  14. 14.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3 (2003)Google Scholar
  15. 15.
    Brickley, D., Miller, L.: The Friend of a Friend (FOAF) project,
  16. 16.
    Jarvelin, K., Kekalainen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR 2000 (2000)Google Scholar
  17. 17.
    Neville, J., Gallagher, B., Eliassi-Rad, T.: Evaluating statistical tests for within-network classifiers of relational data. In: ICDM 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Siemens AG, Corporate TechnologyMunichGermany
  2. 2.Ludwig-Maximilians-Universität MünchenMunichGermany
  3. 3.Karlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations