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Manually querying search engines in order to acquire a large body of related information is a tedious, error-prone process. Search engines retrieve and rank potentially relevant documents for human perusal, but do not extract facts, assess confidence, or fuse information from multiple documents. This paper present an information extraction system that aims to automate the tedious process of extracting large collections of facts from large-scale, domain-independent, and scalable manner. The paper focus on four major components: search engine interface, extractor, assessor, database, and further analyzes system architecture and reports on simulation results with large-scale information extraction systems.
Communications and Information Technology, 2005. ISCIT 2005. IEEE International Symposium on (Volume:2 )
Date of Conference: 12-14 Oct. 2005