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Web-Scale Information Extraction in KnowItAll (Preliminary Results)







Web-Scale Information Extraction in KnowItAll
(Preliminary Results) 















Manually querying search engines in order to accumulate a large
body of factual information is a tedious, error-prone process of
piecemeal search. Search engines retrieve and rank potentially rel-
evant documents for human perusal, but do not extract facts, assess
confidence, or fuse information from multiple documents. This pa-
per introduces K
NOWITALL, a system that aims to automate the
tedious process of extracting large collections of facts from the web
in an autonomous, domain-independent, and scalable manner.



The paper describes preliminary experiments in which an in-
stance of K
NOWITALL, running for four days on a single machine,
was able to automatically extract 54,753 facts. K
NOWITALL asso-
ciates a probability with each fact enabling it to trade off precision
and recall. The paper analyzes K
NOWITALL’s architecture and re-
ports on lessons learned for the design of large-scale information
extraction systems. 



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