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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| Vydané v: | The VLDB journal Ročník 24; číslo 6; s. 707 - 730 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2015
Springer |
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| 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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| Keywords | Inductive logic programming ILP Knowledge bases Rule mining |
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