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2005
Springer

ExpertiseNet: Relational and Evolutionary Expert Modeling

9 years 3 months ago
ExpertiseNet: Relational and Evolutionary Expert Modeling
We develop a novel user-centric modeling technology, which can dynamically describe and update a person's expertise profile. In an enterprise environment, the technology can enhance employees' collaboration and productivity by assisting in finding experts, training employees, etc. Instead of using the traditional search methods, such as the keyword match, we propose to use relational and evolutionary graph models, which we call ExpertiseNet, to describe and find experts. These ExpertiseNets are used for mining, retrieval, and visualization. We conduct experiments by building ExpertiseNets for researchers from a research paper collection. The experiments demonstrate that expertise mining and matching are more efficiently achieved based on the proposed relational and evolutionary graph models.
Xiaodan Song, Belle L. Tseng, Ching-Yung Lin, Ming
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where UM
Authors Xiaodan Song, Belle L. Tseng, Ching-Yung Lin, Ming-Ting Sun
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