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Conference papers

Fast and Exact Rule Mining with AMIE 3

Jonathan Lajus 1, 2, 3 Luis Galárraga 4 Fabian Suchanek 1, 2, 3
1 DIG - Data, Intelligence and Graphs
LTCI - Laboratoire Traitement et Communication de l'Information
4 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Given a knowledge base (KB), rule mining finds rules such as "If two people are married, then they live (most likely) in the same place". Due to the exponential search space, rule mining approaches still have difficulties to scale to today's large KBs. In this paper, we present AMIE 3, a system that employs a number of sophisticated pruning strategies and optimizations. This allows the system to mine rules on large KBs in a matter of minutes. Most importantly, we do not have to resort to approximations or sampling, but are able to compute the exact confidence and support of each rule. Our experiments on DBpedia, YAGO, and Wikidata show that AMIE 3 beats the state of the art by a factor of more than 15 in terms of runtime.
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https://hal.inria.fr/hal-03084606
Contributor : Luis Galárraga <>
Submitted on : Monday, December 21, 2020 - 11:31:48 AM
Last modification on : Monday, February 22, 2021 - 9:23:39 PM

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Jonathan Lajus, Luis Galárraga, Fabian Suchanek. Fast and Exact Rule Mining with AMIE 3. ESWC 2020: The Semantic Web, Jun 2020, Virtual Event, Greece. pp.36-52, ⟨10.1007/978-3-030-49461-2_3⟩. ⟨hal-03084606⟩

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