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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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
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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.
AbstractList	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 [16] 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. 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.
Author	Suchanek, Fabian M. Hose, Katja Galárraga, Luis Teflioudi, Christina
Author_xml	– sequence: 1 givenname: Luis surname: Galárraga fullname: Galárraga, Luis email: galarrag@enst.fr organization: Télécom ParisTech – sequence: 2 givenname: Christina surname: Teflioudi fullname: Teflioudi, Christina organization: Max Planck Institute for Informatics – sequence: 3 givenname: Katja surname: Hose fullname: Hose, Katja organization: Aalborg University – sequence: 4 givenname: Fabian M. surname: Suchanek fullname: Suchanek, Fabian M. organization: Télécom ParisTech
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Snippet	Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive logic... Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic...
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Title	Fast rule mining in ontological knowledge bases with AMIE
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