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

摘要

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 coauthor relationship. To further group the collaborative communities of authors according to research interests, the CiteSeer~X 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 topic-based 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 experimental results show that 81.61% of the topic-based collaborative communities satisfy the consistency requirement. On the other hand, the accuracy of the discovered sub-concept relationship is verified by checking the Wikipedia categories. It is shown that 75.96% of the sub-concept terms are properly assigned in the concept hierarchy.