Adaptive Community Identification on Semantic Social Networks with Contextual Synchronization: An Empirical Study

摘要

To support prompt collaborations, an ontology-based social network platform has been proposed to find the most relevant users by context representation (i.e., personal and group contexts) and matching. Consequently, groups can be dynamically organized with respect to the similarities among the personal contexts by context synchronization. Individual users can engage in complex collaborations related to multiple semantics. In this paper, we want to show and discuss the experimental results collected from a collaborative information searching system with context synchronization. Main empirical issues are ⅰ) setting thresholds, ⅱ) searching performance, and ⅲ) scalability testing.