TY - GEN
T1 - AMIE
T2 - 22nd International Conference on World Wide Web, WWW 2013
AU - Galárraga, Luis
AU - Teflioudi, Christina
AU - Hose, Katja
AU - Suchanek, Fabian M.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a ma- chine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. In this paper, we develop a rule mining model that is explicitly tailored to support the OWA scenario. It is inspired by association rule mining and introduces a novel measure for confidence. Our extensive experiments show that our approach outperforms state-of-the-art approaches in terms of precision and cover- age. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
AB - Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a ma- chine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. In this paper, we develop a rule mining model that is explicitly tailored to support the OWA scenario. It is inspired by association rule mining and introduces a novel measure for confidence. Our extensive experiments show that our approach outperforms state-of-the-art approaches in terms of precision and cover- age. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
KW - ILP
KW - Inductive logic programming
KW - Rule mining
M3 - Conference contribution
AN - SCOPUS:84880561534
SN - 9781450320351
T3 - WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
SP - 413
EP - 422
BT - WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
Y2 - 13 May 2013 through 17 May 2013
ER -