Generating Possible Interpretations for Statistics from Linked Open Data

  • Heiko Paulheim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in one city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.

Keywords

Association Rule User Study Rule Learning Entity Recognition Link Open Data 
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

  • Heiko Paulheim
    • 1
  1. 1.Knowledge Engineering GroupTechnische Universität DarmstadtGermany

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