ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries

  • Hannes Schulz
  • Kristian Kersting
  • Andreas Karwath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5989)


Relational data is complex. This complexity makes one of the basic steps of ILP difficult: understanding the data and results. If the user cannot easily understand it, he draws incomplete conclusions. The situation is very much as in the parable of the blind men and the elephant that appears in many cultures. In this tale the blind work independently and with quite different pieces of information, thereby drawing very different conclusions about the nature of the beast. In contrast, visual representations make it easy to shift from one perspective to another while exploring and analyzing data. This paper describes a method for embedding interpretations and queries into a single, common Euclidean space based on their co-proven statistics. We demonstrate our method on real-world datasets showing that ILP results can indeed be captured at a glance.


Environmental Estrogen Weighted Neighbour Instance Base Learning Inductive Logic Program Common Query 
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 2010

Authors and Affiliations

  • Hannes Schulz
    • 1
  • Kristian Kersting
    • 2
  • Andreas Karwath
    • 1
  1. 1.Institut für InformatikAlbert-Ludwigs UniversitätFreiburgGermany
  2. 2.Dept. of Knowledge DiscoveryFraunhofer IAIS, Schloss BirlinghovenSt AugustinGermany

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