Web-scale information extraction in knowitall

• DOI: 10.1145/988672.988687
• Publication Date: 2004-07-20T15:55:38Z
• Authors: Oren Etzioni; Michael Cafarella; Doug Downey; Stanley Kok; Ana-Maria Popescu; Tal Shaked; Stephen Soderland; Daniel S. Weld; Alexander Yates
• Keywords: Fuse (electrical); Rank (graph theory)

Manually querying search engines in order to accumulate a large bodyof factual information is tedious, error-prone process of piecemealsearch. Search retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse from multiple documents. This paperintroduces KnowItAll, system that aims automate the tedious ofextracting collections facts web an autonomous,domain-independent, scalable manner.The paper describes preliminary experiments which instance running four days on single machine, was able automatically 54,753 facts. KnowItAll associates probability with each fact enabling it trade off precision recall. The analyzes KnowItAll's architecture reports lessons learned design large-scale extraction systems.