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

Hannes Schulz; Kristian Kersting; Andreas Karwath
In: Luc De Raedt (Hrsg.). Inductive Logic Programming, 19th International Conference. International Conference on Inductive Logic Programming (ILP-2009), July 2-4, Leuven, Belgium, Pages 209-216, Lecture Notes in Computer Science, Vol. 5989, Springer, 2009.


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.

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