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Fast and Exact Rule Mining with AMIE 3

  • Institut Polytechnique de Paris
  • INRIA Institut National de Recherche en Informatique et en Automatique

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

86 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationThe Semantic Web - 17th International Conference, ESWC 2020, Proceedings
EditorsAndreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, Michael Cochez
PublisherSpringer
Pages36-52
Number of pages17
ISBN (Print)9783030494605
DOIs
Publication statusPublished - 1 Jan 2020
Event17th Extended Semantic Web Conference, ESWC 2020 - Heraklion, Greece
Duration: 31 May 2020 → 4 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12123 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Extended Semantic Web Conference, ESWC 2020
Country/TerritoryGreece
CityHeraklion
Period31/05/20 → 4/06/20

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