Abstract
We study complementary navigation of news and blog, where Wikipedia entries are utilized as fundamental knowledge source for linking news articles and blog feeds/posts. In the proposed framework, given a topic as the title of a Wikipedia entry, its Wikipedia entry body text is analyzed as fundamental knowledge source for the given topic, and terms strongly related to the given topic are extracted. Those terms are then used for ranking news articles and blog posts. In the scenario of complementary navigation from a news article to closely related blog posts, Japanese Wikipedia entries are ranked according to the number of strongly related terms shared by the given news article and each Wikipedia entry. Then, top ranked 10 entries are regarded as indices for further retrieving closely related blog posts. The retrieved blog posts are finally ranked all together. The retrieved blog posts are then shown to users as blogs of personal opinions and experiences that are closely related to the given news article. In our preliminary evaluation, through an interface for manually selecting relevant Wikipedia entries, the rate of successfully retrieving relevant blog posts improved.
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Sato, Y., Yokomoto, D., Nakasaki, H., Kawaba, M., Utsuro, T., Fukuhara, T. (2010). Linking Topics of News and Blogs with Wikipedia for Complementary Navigation. In: Breslin, J.G., Burg, T.N., Kim, HG., Raftery, T., Schmidt, JH. (eds) Recent Trends and Developments in Social Software. BlogTalk BlogTalk 2008 2009. Lecture Notes in Computer Science, vol 6045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16581-8_8
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DOI: https://doi.org/10.1007/978-3-642-16581-8_8
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