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Title: A Wikipedia based semantic graph model for topic tracking in blogosphere
Authors: Tang, Jintao
Wang, Ting
Lu, Qin
Wang, Ji
Li, Wenjie
Issue Date: Jul-2011
Publisher: AAAI Press/International Joint Conferences on Artificial Intelligence
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.
Rights: Copyright © 2011 International Joint Conferences on Artificial Intelligence
Posted with permission of the publisher.
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
Type: Conference Paper
DOI: 10.5591/978-1-57735-516-8/IJCAI11-389
ISBN: 978-1-57735-515-1
Appears in Collections:COMP Conference Papers & Presentations

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