Subject Headings: Web-based Information Extraction.
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Abstract
- Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.
1.1 Previous Work
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- KNOWITALL is able to use weaker input than previous IE systems in part because, rather than extracting information from complex and potentially difficult-to-understand texts, KNOWITALL relies on the scale and redundancy of the web for an ample supply of simple sentences that are relatively easy to process. This notion of “redundancy-based extraction” was introduced in Mulder [17] and further articulated in AskMSR [2].