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

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Abstract

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.

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Notes

  1. http://www.wikidata.org.

  2. RDF schema has only positive rules and no disjointness constraints or similar concepts.

  3. http://www.cs.ox.ac.uk/activities/machlearn/Aleph/aleph_toc.html.

  4. In these cases, the pruning precision in Table 6 was computed by comparing the output of AMIE\(+\) to the output of AMIE on the mined subset.

  5. We used the YAGO3 [28] types because the type signatures in older versions of YAGO were too general. For example, the relation livesIn is defined from person to location in YAGO2s, whereas in YAGO3 it is defined from person to city.

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Acknowledgments

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

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Authors and Affiliations

  1. Télécom ParisTech, Paris, France

    Luis Galárraga & Fabian M. Suchanek

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Christina Teflioudi

  3. Aalborg University, Aalborg, Denmark

    Katja Hose

Authors
  1. Luis Galárraga
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  2. Christina Teflioudi
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  3. Katja Hose
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  4. Fabian M. Suchanek
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Correspondence to Luis Galárraga.

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Galárraga, L., Teflioudi, C., Hose, K. et al. Fast rule mining in ontological knowledge bases with AMIE\(+\) . The VLDB Journal 24, 707–730 (2015). https://doi.org/10.1007/s00778-015-0394-1

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