Automatic extraction of efficient axiom sets from large knowledge bases

Abhishek Sharma, Kenneth D Forbus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Efficient reasoning in large knowledge bases is an important problem for AI systems. Hand-optimization of reasoning becomes impractical as KBs grow, and impossible as knowledge is automatically added via knowledge capture or machine learning. This paper describes a method for automatic extraction of axioms for efficient inference over large knowledge bases, given a set of query types and information about the types of facts in the KB currently as well as what might be learned. We use the highly right skewed distribution of predicate connectivity in large knowledge bases to prune intractable regions of the search space. We show the efficacy of the se techniques via experiments using queries from a learning by reading system. Results show that the se methods lead to an order of magnitude improvement in time with minimal loss in coverage.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Pages1248-1254
Number of pages7
StatePublished - Dec 1 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: Jul 14 2013Jul 18 2013

Other

Other27th AAAI Conference on Artificial Intelligence, AAAI 2013
CountryUnited States
CityBellevue, WA
Period7/14/137/18/13

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Sharma, A., & Forbus, K. D. (2013). Automatic extraction of efficient axiom sets from large knowledge bases. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 (pp. 1248-1254)