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　　The learned facts are continuously added to NELLs growing database, which the researchers call a knowledge base. A larger pool of facts, Dr. Mitchell says, will help refine NELLs learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.

　　NELL is one project in a widening field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies formal descriptions of concepts and relationships to help computers mimic human understanding. The ideal has been discussed for years, and more than a decade ago Sir Tim Berners-Lee, who invented the underlying software for the World Wide Web, sketched his vision of a semantic Web.

　　Today, ever-faster computers, an explosion of Web data and improved software techniques are opening the door to rapid progress. Scientists at universities, government labs, Google, Microsoft, I.B.M. and elsewhere are pursuing breakthroughs, along somewhat different paths.

　　For example, I.B.M.s question answering machine, Watson, shows remarkable semantic understanding in fields like history, literature and sports as it plays the quiz show Jeopardy! Google Squared, a research project at the Internet search giant, demonstrates ample grasp of semantic categories as it finds and presents information from around the Web on search topics like U.S. presidents and cheeses.