Wednesday, March 09, 2005
Google practices dividing to conquer | Tech News on ZDNet:
"By Stefanie Olsen, CNET News.com
SAN FRANCISCO--Google's 8 billion-plus Web document index may not multiply, but its search engine will learn to better divide the data.
That was part of the message from Peter Norvig, Google's director of search quality, who on Tuesday gave a keynote speech here at the Semantic Technology Conference. Norvig, a former NASA employee and an author of books on artificial intelligence, highlighted several research projects the company is developing to help classify data and improve the relevance of search results...
Norvig highlighted a research paper written by a Google employee last year regarding a classification engine the company is testing. The technology can parse a proper noun or compound nouns into several categories in order to deliver clustered results, for example. For a query on "ATM," or asynchronous transfer mode, the engine would be able to use the terms "such as" on Web pages indexed with the term to discover that it can be linked to the expression "high-speed networks." As a result, a search for high-speed networks might pull up a cluster on ATM.
Norvig said the same technology could be used to mine factual answers from the Web for queries like "President Lincoln's birth date." The technique could offer an edge over Microsoft's recent addition of encyclopedic answers to its database, thanks to its Encarta software, Norvig said. That's because MSN's engine could miss the chance to deliver the desired factual answer if the searcher's query is inexact. In contrast, Google draws on the semantic Web and various language sets from pages to find a match..."
"By Stefanie Olsen, CNET News.com
SAN FRANCISCO--Google's 8 billion-plus Web document index may not multiply, but its search engine will learn to better divide the data.
That was part of the message from Peter Norvig, Google's director of search quality, who on Tuesday gave a keynote speech here at the Semantic Technology Conference. Norvig, a former NASA employee and an author of books on artificial intelligence, highlighted several research projects the company is developing to help classify data and improve the relevance of search results...
Norvig highlighted a research paper written by a Google employee last year regarding a classification engine the company is testing. The technology can parse a proper noun or compound nouns into several categories in order to deliver clustered results, for example. For a query on "ATM," or asynchronous transfer mode, the engine would be able to use the terms "such as" on Web pages indexed with the term to discover that it can be linked to the expression "high-speed networks." As a result, a search for high-speed networks might pull up a cluster on ATM.
Norvig said the same technology could be used to mine factual answers from the Web for queries like "President Lincoln's birth date." The technique could offer an edge over Microsoft's recent addition of encyclopedic answers to its database, thanks to its Encarta software, Norvig said. That's because MSN's engine could miss the chance to deliver the desired factual answer if the searcher's query is inexact. In contrast, Google draws on the semantic Web and various language sets from pages to find a match..."
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