A wikipedia based semantic graph model for topic tracking in blogosphere

Jintao Tang, Ting Wang, Qin Lu, Ji Wang, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Number of pages6
Publication statusPublished - 1 Dec 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: 16 Jul 201122 Jul 2011


Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CityBarcelona, Catalonia

ASJC Scopus subject areas

  • Artificial Intelligence

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