COALA – Correlation-Aware Active Learning of Link Specifications

  • Axel-Cyrille Ngonga Ngomo
  • Klaus Lyko
  • Victor Christen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

Link Discovery plays a central role in the creation of knowledge bases that abide by the five Linked Data principles. Over the last years, several active learning approaches have been developed and used to facilitate the supervised learning of link specifications. Yet so far, these approaches have not taken the correlation between unlabeled examples into account when requiring labels from their user. In this paper, we address exactly this drawback by presenting the concept of the correlation-aware active learning of link specifications. We then present two generic approaches that implement this concept. The first approach is based on graph clustering and can make use of intra-class correlation. The second relies on the activation-spreading paradigm and can make use of both intra- and inter-class correlations. We evaluate the accuracy of these approaches and compare them against a state-of-the-art link specification learning approach in ten different settings. Our results show that our approaches outperform the state of the art by leading to specifications with higher F-scores.

Keywords

Active Learning Link Discovery Genetic Programming 

References

  1. 1.
    Auer, S., Lehmann, J., Ngonga Ngomo, A.-C.: Introduction to linked data and its lifecycle on the web. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 1–75. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Bodó, Z., Minier, Z., Csató, L.: Active learning with clustering. Journal of Machine Learning Research - Proceedings Track 16, 127–139 (2011)Google Scholar
  3. 3.
    Euzenat, J., Ferrara, A., van Hage, W.R., Hollink, L., Meilicke, C., Nikolov, A., Ritze, D., Scharffe, F., Shvaiko, P., Stuckenschmidt, H., Sváb-Zamazal, O., dos Santos, C.T.: Results of the ontology alignment evaluation initiative 2011. In: OM (2011)Google Scholar
  4. 4.
    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: 2010 IEEE Congress on Evolutionary Computation Evolutionary Computation (CEC), pp. 1–8 (2010)Google Scholar
  5. 5.
    Isele, R., Bizer, C.: Learning linkage rules using genetic programming. In: OM. CEUR Workshop Proceedings, vol. 814 (2011)Google Scholar
  6. 6.
    Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery without losing recall. In: Marian, A., Vassalos, V. (eds.) WebDB (2011)Google Scholar
  7. 7.
    Isele, R., Jentzsch, A., Bizer, C.: Active learning of expressive linkage rules for the web of data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 411–418. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Köpcke, H., Thor, A., Rahm, E.: Comparative evaluation of entity resolution approaches with fever. Proc. VLDB Endow. 2(2), 1574–1577 (2009)Google Scholar
  9. 9.
    Morsey, M., Lehmann, J., Auer, S., Ngonga Ngomo, A.-C.: DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data. 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. 454–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Ngonga Ngomo, A.C.: Parameter-free clustering of protein-protein interaction graphs. In: Proceedings of MLSB Symposium (2010)Google Scholar
  11. 11.
    Ngonga Ngomo, A.-C.: Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 378–393. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Ngonga Ngomo, A.C.: On link discovery using a hybrid approach. Journal on Data Semantics 1, 203–217 (2012)CrossRefGoogle Scholar
  13. 13.
    Ngonga Ngomo, A.C., Auer, S.: LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data. In: Proceedings of IJCAI, pp. 2312–2317 (2011)Google Scholar
  14. 14.
    Ngonga Ngomo, A.C., Lehmann, J., Auer, S., Höffner, K.: RAVEN – Active Learning of Link Specifications. In: Proceedings of OM@ISWC (2011)Google Scholar
  15. 15.
    Ngonga Ngomo, A.-C., Lyko, K.: EAGLE: Efficient active learning of link specifications using genetic programming. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 149–163. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Ngonga Ngomo, A.-C., Schumacher, F.: BorderFlow: A local graph clustering algorithm for natural language processing. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp. 547–558. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Nikolov, A., d’Aquin, M., Motta, E.: Unsupervised learning of link discovery configuration. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 119–133. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)Google Scholar
  19. 19.
    Shekarpour, S., Auer, S., Ngonga Ngomo, A.C., Gerber, D., Hellmann, S., Stadler, C.: Keyword-driven sparql query generation leveraging background knowledge. In: International Conference on Web Intelligence (2011)Google Scholar
  20. 20.
    Unger, C., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.-C., Gerber, D., Cimiano, P.: Sparql template-based question answering. In: Proceedings of WWW (2012)Google Scholar
  21. 21.
    Xiao, C., Wang, W., Lin, X., Yu, J.X.: Efficient similarity joins for near duplicate detection. In: WWW, pp. 131–140 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Klaus Lyko
    • 1
  • Victor Christen
    • 1
  1. 1.Department of Computer Science, AKSW Research GroupUniversity of LeipzigGermany

Personalised recommendations