Towards Nonmonotonic Relational Learning from Knowledge Graphs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10326)


Recent advances in information extraction have led to the so-called knowledge graphs (KGs), i.e., huge collections of relational factual knowledge. Since KGs are automatically constructed, they are inherently incomplete, thus naturally treated under the Open World Assumption (OWA). Rule mining techniques have been exploited to support the crucial task of KG completion. However, these techniques can mine Horn rules, which are insufficiently expressive to capture exceptions, and might thus make incorrect predictions on missing links. Recently, a rule-based method for filling in this gap was proposed which, however, applies to a flattened representation of a KG with only unary facts. In this work we make the first steps towards extending this approach to KGs in their original relational form, and provide preliminary evaluation results on real-world KGs, which demonstrate the effectiveness of our method.


Horn Rules Relational Association Rule Mining Nonmonotonic Rule Nominal Exclusion Theory Revision 
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.



We thank anonymous reviewers for their insightful suggestions and Jacopo Urbani for his helpful comments on an earlier version of this paper.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Max-Planck Institute for InformaticsSaarbrückenGermany
  2. 2.Università degli Studi di Bari Aldo MoroBariItaly

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