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Title: A Wikipedia based semantic graph model for topic tracking in blogosphere
Authors: Tang, J
Wang, T
Lu, Q 
Wang, J
Li, W 
Issue Date: Jul-2011
Source: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, 16-22 July 2011, v. 3, 2337-2342
Abstract: There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the “word-of-mouth” effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.
Publisher: AAAI Press/International Joint Conferences on Artificial Intelligence
ISBN: 978-1-57735-515-1
DOI: 10.5591/978-1-57735-516-8/IJCAI11-389
Rights: Copyright © 2011 International Joint Conferences on Artificial Intelligence
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Appears in Collections:Conference Paper

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