SAIM – One Step Closer to Zero-Configuration Link Discovery

  • Klaus Lyko
  • Konrad Höffner
  • René Speck
  • Axel-Cyrille Ngonga Ngomo
  • Jens Lehmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)

Abstract

Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.

Keywords

Interlinking Machine Learning Data Integration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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.
    Morsey, M., Lehmann, J., Auer, S., Stadler, C., Hellmann, S.: DBpedia and the live extraction of structured data from wikipedia. Program: Electronic Library and Information Systems 46, 27 (2012)CrossRefGoogle Scholar
  3. 3.
    Ngonga Ngomo, A.-C.: On link discovery using a hybrid approach. Journal on Data Semantics 1, 203–217 (2012)CrossRefGoogle Scholar
  4. 4.
    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 (2011)Google Scholar
  5. 5.
    Ngonga Ngomo, A.-C., Lehmann, J., Auer, S., Höffner, K.: RAVEN – Active Learning of Link Specifications. In: Proceedings of OM@ISWC, vol. 814 (2011)Google Scholar
  6. 6.
    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
  7. 7.
    Ngonga Ngomo, A.-C., Lyko, K., Christen, V.: COALA – correlation-aware active learning of link specifications. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 442–456. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Klaus Lyko
    • 1
  • Konrad Höffner
    • 1
  • René Speck
    • 1
  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Jens Lehmann
    • 1
  1. 1.Universität LeipzigLeipzigGermany

Personalised recommendations