Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Declarative Bias for Specific-to-General ILP Systems
Machine Learning - Special issue on bias evaluation and selection
Fast discovery of association rules
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Discovery of relational association rules
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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Yago: a core of semantic knowledge
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ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
RDF-3X: a RISC-style engine for RDF
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EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
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Knowledge-Based Systems
PARIS: probabilistic alignment of relations, instances, and schema
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Reconciling ontologies and the web of data
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YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
Artificial Intelligence
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Mining frequent neighborhood patterns in a large labeled graph
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The essence of knowledge (bases) through entity rankings
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A study of the knowledge base requirements for passing an elementary science test
Proceedings of the 2013 workshop on Automated knowledge base construction
Mining rules to align knowledge bases
Proceedings of the 2013 workshop on Automated knowledge base construction
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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 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 coverage. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches.