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EAGLE: Efficient Active Learning of Link Specifications Using Genetic Programming

  • Axel-Cyrille Ngonga Ngomo
  • Klaus Lyko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Most Link Discovery frameworks implement approaches that require two main computational steps. First, a link specification has to be explicated by the user. Then, this specification must be executed. While several approaches for the time-efficient execution of link specifications have been developed over the last few years, the discovery of accurate link specifications remains a tedious problem. In this paper, we present EAGLE, an active learning approach based on genetic programming. EAGLE generates highly accurate link specifications while reducing the annotation burden for the user. We evaluate EAGLE against batch learning on three different data sets and show that our algorithm can detect specifications with an F-measure superior to 90% while requiring a small number of questions.

Keywords

Active Learning Genetic Program Record Linkage Entity Resolution Fuzzy Decision Tree 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Klaus Lyko
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany

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