Journal
KNOWLEDGE-BASED SYSTEMS
Volume 23, Issue 6, Pages 615-625Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2010.04.008
Keywords
Temporal expert finding; Conferences influence; Generalized time topic modeling; Unsupervised learning
Categories
Funding
- National Natural Science Foundation of China [60973102, 60703059]
- Chinese National Key Foundation Research and Development Plan [2007CB310803]
- Higher Education Commission, Islamabad, Pakistan
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This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts' interests and relationships change over time Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information (C) 2010 Elsevier B.V. All rights reserved.
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