Hierarchical Topic-Based Communities Construction for Authors in a Literature Database

In this paper, given a set of research papers with only title and author information, a mining strategy is proposed to discover and organize the communities of authors according to both the co-author relationships and research topics of their published papers. The proposed method applies the CONGA algorithm to discover collaborative communities from the network constructed from the co-author relationship. To further group the collaborative communities of authors according to research interests, the CiteSeerX is used as an external source to discover the hidden hierarchical relationships among the topics covered by the papers. In order to evaluate whether the constructed topicbased collaborative community is semantically meaningful, the first part of evaluation is to measure the consistency between the terms appearing in the published papers of a topic-based collaborative community and the terms in the documents related to the specific topic retrieved from other external source. The

• Type: Journal
• Year: 2010
• Where: IEAAIE
• Authors: Chien-Liang Wu, Jia-Ling Koh
• Keywords: Artificial Intelligence, Co-author Relationship, Collaborative Communities, Collaborative Community, IEAAIE 2010