A Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2337-2341.
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 named entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through a 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 using real-world blog data. Experimental results show the advantage of the proposed method on tracking topics in short, noisy text.
@inproceedings{ijcai2011p389,
title = {A Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere},
author = {Tang, Jintao and Wang, Ting and Lu, Qin and Wang, Ji and Li, Wenjie},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {2337--2341},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-389},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-389},
}
TY - CPAPER TI - A Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere AU - Tang, Jintao AU - Wang, Ting AU - Lu, Qin AU - Wang, Ji AU - Li, Wenjie T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 2337 EP - 2341 DO - 10.5591/978-1-57735-516-8/IJCAI11-389 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-389 ER -Copied
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