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.

• Type: Conference
• Year: 2005
• Where: UM
• Authors: Xiaodan Song, Belle L. Tseng, Ching-Yung Lin, Ming-Ting Sun
• Keywords: Evolutionary Graph Models, Person's Expertise Profile, Traditional Search Methods, UM 2005