Software that generates a list of reading material tailored to a person’s individual interests has been developed by a PhD student in the US…”Increasingly, a net user who wants to learn more about a subject will read its Wikipedia page,” he adds. “However, for further depth in the subject, there has been no system for advising the user which other [Wikipedia] articles to read, and in which order.”So Wissner-Gross experimented with algorithms that analyse the hypertext link structure of the site. He used these to find the “most important” Wikipedia pages on a particular topic. He also used them to find pages within a particular area, like physics, that also had information about another topic of interest, such as helicopters, say. An algorithm similar to that used by Google was particularly effective, he found. It assesses page popularity by examining the number of other pages that link to it and also the popularity of those pages. Another algorithm, that examines the number of links needed to get from one article to another, also produced good results with shorter lists.
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Researchers at the Technion-Israel Institute of Technology have found a way to give computers encyclopedic knowledge of the world to help them “think smarter,” making common sense and broad-based connections between topics just as the human mind does….AThe program devised by the Technion researchers helps computers map single words and larger fragments of text to a database of concepts built from the online encyclopedia Wikipedia, which has over one million articles in its English-language version. The Wikipedia-based concepts act as “background knowledge” to help computers figure out the meaning of the text entered into a Web search, for instance.
Read this article from Physorg.com