Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures

  • Axel-Cyrille Ngonga Ngomo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)


Time-efficient algorithms are essential to address the complex linking tasks that arise when trying to discover links on the Web of Data. Although several lossless approaches have been developed for this exact purpose, they do not offer theoretical guarantees with respect to their performance. In this paper, we address this drawback by presenting the first Link Discovery approach with theoretical quality guarantees. In particular, we prove that given an achievable reduction ratio r, our Link Discovery approach \(\mathcal{HR}^3\) can achieve a reduction ratio r′ ≤ r in a metric space where distances are measured by the means of a Minkowski metric of any order p ≥ 2. We compare \(\mathcal{HR}^3\) and the HYPPO algorithm implemented in LIMES 0.5 with respect to the number of comparisons they carry out. In addition, we compare our approach with the algorithms implemented in the state-of-the-art frameworks LIMES 0.5 and SILK 2.5 with respect to runtime. We show that \(\mathcal{HR}^3\) outperforms these previous approaches with respect to runtime in each of our four experimental setups.


Genetic Programming Reduction Ratio Indexing Scheme Theoretical Guarantee Minkowski Distance 
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
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany

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