Rule Learning from Knowledge Graphs Guided by Embedding Models

  • Vinh Thinh Ho
  • Daria Stepanova
  • Mohamed H. Gad-Elrab
  • Evgeny Kharlamov
  • Gerhard Weikum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)


Rules over a Knowledge Graph (KG) capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules could be generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora. Experiments on real-world KGs demonstrate the effectiveness of our novel approach both with respect to the quality of the learned rules and fact predictions that they produce.



This work was partially supported by the EPSRC projects DBOnto, MaSI\(^3\) and ED\(^3\).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vinh Thinh Ho
    • 1
  • Daria Stepanova
    • 1
  • Mohamed H. Gad-Elrab
    • 1
  • Evgeny Kharlamov
    • 2
  • Gerhard Weikum
    • 1
  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany
  2. 2.University of OxfordOxfordUK

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