KnowItNow: fast, scalable information extraction from the web

  • Authors:

  • Michael J. Cafarella;Doug Downey;Stephen Soderland;Oren Etzioni

  • Affiliations:

  • University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA

  • Venue:
  • HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
  • Year:
  • 2005

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Abstract

Numerous NLP applications rely on search-engine queries, both to extract information from and to compute statistics over the Web corpus. But search engines often limit the number of available queries. As a result, query-intensive NLP applications such as Information Extraction (IE) distribute their query load over several days, making IE a slow, offline process.This paper introduces a novel architecture for IE that obviates queries to commercial search engines. The architecture is embodied in a system called KnowItNow that performs high-precision IE in minutes instead of days. We compare KnowItNow experimentally with the previously-published KnowItAll system, and quantify the tradeoff between recall and speed. KnowItNow's extraction rate is two to three orders of magnitude higher than KnowItAll's.