Active Learning of Expressive Linkage Rules for the Web of Data

  • Robert Isele
  • Anja Jentzsch
  • Christian Bizer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7387)

Abstract

The amount of data that is available as Linked Data on the Web has grown rapidly over the last years. However, the linkage between data sources remains sparse as setting RDF links means effort for the data publishers. Many existing methods for generating these links rely on explicit linkage rules which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and required expertise to write linkage rules, we present an approach which combines genetic programming and active learning for the interactive generation of expressive linkage rules. Our approach automates the generation of a linkage rule and only requires the user to confirm or decline a number of example links. The algorithm minimizes user involvement by selecting example links which yield a high information gain. The proposed approach has been implemented in the Silk Link Discovery Framework. Within our experiments, the algorithm was capable of finding linkage rules with a full F1-measure by asking the user to confirm or decline a maximum amount of 20 links.

References

  1. 1.
    Arasu, A., Götz, M., Kaushik, R.: On active learning of record matching packages. In: Proceedings of the 2010 International Conference on Management of Data, SIGMOD 2010, pp. 783–794. ACM, New York (2010)CrossRefGoogle Scholar
  2. 2.
    Carvalho, M., Laender, A., Gonçalves, M., da Silva, A.: Replica identification using genetic programming. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1801–1806. ACM (2008)Google Scholar
  3. 3.
    de Carvalho, M.G., Gonçalves, M.A., Laender, A.H.F., da Silva, A.S.: Learning to deduplicate. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2006, pp. 41–50. ACM, New York (2006)CrossRefGoogle Scholar
  4. 4.
    de Carvalho, M.G., Laender, A.H.F., Goncalves, M.A., da Silva, A.S.: A genetic programming approach to record deduplication. IEEE Transactions on Knowledge and Data Engineering 99(preprints) (2010)Google Scholar
  5. 5.
    de Freitas, J., Pappa, G., da Silva, A., Gonçalves, M., Moura, E., Veloso, A., Laender, A., de Carvalho, M.: Active learning genetic programming for record deduplication. In: Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)Google Scholar
  6. 6.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer (2007)Google Scholar
  7. 7.
    Heath, T., Bizer, C.: Linked data: Evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology 1(1), 1–136 (2011)CrossRefGoogle Scholar
  8. 8.
    Isele, R., Bizer, C.: Learning linkage rules using genetic programming. In: 6th International Workshop on Ontology Matching, Bonn, Germany (2011)Google Scholar
  9. 9.
    Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery without losing recall. In: 14th International Workshop on the Web and Databases (WebDB 2011), Athens (2011)Google Scholar
  10. 10.
    Koza, J., Keane, M., Streeter, M., Mydlowec, W., Yu, J., Lanza, G.: Genetic programming IV: Routine human-competitive machine intelligence. Springer (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Robert Isele
    • 1
  • Anja Jentzsch
    • 1
  • Christian Bizer
    • 1
  1. 1.Web-based Systems GroupFreie Universität BerlinBerlinGermany

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