The VLDB Journal

, Volume 24, Issue 6, pp 707–730 | Cite as

Fast rule mining in ontological knowledge bases with AMIE\(+\)

  • Luis GalárragaEmail author
  • Christina Teflioudi
  • Katja Hose
  • Fabian M. Suchanek
Regular Paper


Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive logic programming (ILP) can be used to mine logical rules from these KBs, such as “If two persons are married, then they (usually) live in the same city.” While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open-world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE (Galárraga et al. in WWW, 2013) has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. Extensive experiments show how our new approach, AMIE\(+\), extends to areas of mining that were previously beyond reach.


Rule mining Inductive logic programming ILP Knowledge bases 



This work is supported by the “Chair Machine Learning for Big Data” of Télécom ParisTech.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Luis Galárraga
    • 1
    Email author
  • Christina Teflioudi
    • 2
  • Katja Hose
    • 3
  • Fabian M. Suchanek
    • 1
  1. 1.Télécom ParisTechParisFrance
  2. 2.Max Planck Institute for InformaticsSaarbrückenGermany
  3. 3.Aalborg UniversityAalborgDenmark

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