Open information extraction: the second generation

  • Authors:

  • Oren Etzioni;Anthony Fader;Janara Christensen;Stephen Soderland;Mausam Mausam

  • Affiliations:

  • Turing Center, Department of Computer Science and Engineering, University of Washington, Seattle, WA;Turing Center, Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Turing Center, Department of Computer Science and Engineering, University of Washington, Seattle, WA;Turing Center, Department of Computer Science and Engineering, University of Washington, Seattle, WA

  • Venue:
  • IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
  • Year:
  • 2011

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Abstract

How do we scale information extraction to the massive size and unprecedented heterogeneity of the Web corpus? Beginning in 2003, our KnowItAll project has sought to extract high-quality knowledge from the Web. In 2007, we introduced the Open Information Extraction (Open IE) paradigm which eschews hand-labeled training examples, and avoids domain-specific verbs and nouns, to develop unlexicalized, domain-independent extractors that scale to the Web corpus. Open IE systems have extracted billions of assertions as the basis for both common-sense knowledge and novel question-answering systems. This paper describes the second generation of Open IE systems, which rely on a novel model of how relations and their arguments are expressed in English sentences to double precision/recall compared with previous systems such as TEXTRUNNER and WOE.