Microblog entity linking with social temporal context

Hua, Wen, Zheng, Kai and Zhou, Xiaofang (2015). Microblog entity linking with social temporal context. In: SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, Melbourne VIC, Australia, (1761-1775). 31 May-4 June 2015. doi:10.1145/2723372.2751522


Author Hua, Wen
Zheng, Kai
Zhou, Xiaofang
Title of paper Microblog entity linking with social temporal context
Conference name ACM SIGMOD International Conference on Management of Data, SIGMOD 2015
Conference location Melbourne VIC, Australia
Conference dates 31 May-4 June 2015
Convener Association for Computing Machinery
Proceedings title SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data   Check publisher's open access policy
Journal name Association for Computing Machinery. Special Interest Group on Management of Data. International Conference Proceedings   Check publisher's open access policy
Series Proceedings of the ACM SIGMOD International Conference on Management of Data
Place of Publication New York, NY United States
Publisher Association for Computing Machinery
Publication Year 2015
Sub-type Fully published paper
DOI 10.1145/2723372.2751522
Open Access Status Not Open Access
ISBN 9781450327589
ISSN 0730-8078
Volume 2015-May
Start page 1761
End page 1775
Total pages 15
Chapter number 141
Total chapters 161
Collection year 2016
Language eng
Abstract/Summary Nowadays microblogging sites, such as Twitter and Chinese Sina Weibo, have established themselves as an invaluable information source, which provides a huge collection of manually-generated tweets with broad range of topics from daily life to breaking news. Entity linking is indispensable for understanding and maintaining such information, which in turn facilitates many real-world applications such as tweet clustering and classification, personalized microblog search, and so forth. However, tweets are short, informal and error-prone, rendering traditional approaches for entity linking in documents largely inapplicable. Recent work addresses this problem by utilising information from other tweets and linking entities in a batch manner. Nevertheless, the high computational complexity makes this approach infeasible for real-time applications given the high arrival rate of tweets. In this paper, we propose an efficient solution to link entities in tweets by analyzing their social and temporal context. Our proposed framework takes into consideration three features, namely entity popularity, entity recency, and user interest information embedded in social interactions to assist the entity linking task. Effective indexing structures along with incremental algorithms have also been developed to reduce the computation and maintenance costs of our approach. Experimental results based on real tweet datasets verify the effectiveness and efficiency of our proposals.
Keyword Entity popularity
Entity recency
Microblog entity linking
Social temporal context
User interest
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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