From Strings to Things: Populating Knowledge Bases from Text
June 28, 2016
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The Web is the greatest source of general knowledge available today. Its current form, however, suffers from two limitations. The first is that text and multimedia objects on the Web are easy for people to understand but difficult for machines to interpret and use. The second is that the Web's access paradigm remains dominated by information retrieval, where keyword queries produce a ranked list of documents that must be read to find the desired information. I'll discuss research in natural language understanding and semantic web technologies that addresses both problems by extracting information from text to produce and populate Web-compatible knowledge bases. The resulting knowledge bases have multiple uses: (1) moving the Web's access paradigm from retrieving documents to answering questions, (2) embedding semi-structured knowledge in Web pages in formats designed for computer to understand, (3) providing intelligent computer systems with information they need to perform their tasks, (4) allowing the extracted data and knowledge to be more easily integrated, enabling inference and advanced analytics, and (5) serving as background knowledge to improve text and speech understanding systems.
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