Exception-Enriched Rule Learning from Knowledge Graphs

  • Mohamed H. Gad-ElrabEmail author
  • Daria Stepanova
  • Jacopo Urbani
  • Gerhard Weikum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)


Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. To overcome this problem, we present a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.


Knowledge Graph (KG) Horn Rules Wikidata Nonmonotonic Rule Witness Set 
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 Thomas Eiter, Francesca A. Lisi and the anonymous reviewers for their constructive feedback about this work. The research was partially funded by the NWO VENI project 639.021.335.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mohamed H. Gad-Elrab
    • 1
    Email author
  • Daria Stepanova
    • 1
  • Jacopo Urbani
    • 2
  • Gerhard Weikum
    • 1
  1. 1.Max Planck Institute of InformaticsSaarbrückenGermany
  2. 2.VU University AmsterdamAmsterdamThe Netherlands

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