Fast rule mining in ontological knowledge bases with AMIE

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.”...

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Bibliographic Details
Published in:The VLDB journal Vol. 24; no. 6; pp. 707 - 730
Main Authors: Galárraga, Luis, Teflioudi, Christina, Hose, Katja, Suchanek, Fabian M.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2015
Springer
Subjects:
Computer Science
Database Management
Regular Paper
Web
Inductive logic programming
ILP
Knowledge bases
Rule mining
ISSN:1066-8888, 0949-877X
Online Access:Get full text
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Summary: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.
ISSN:1066-8888
0949-877X
DOI:10.1007/s00778-015-0394-1