Adaptive Community Identification Based on Contextual Synchronization: An Empirical Study

To timely support collaborations between people (agents), an ontology-based platform is proposed to find out the most relevant users, according to their contexts. We have modeled two kinds of contexts (i.e., personal and group contexts) with semantic information derived from ontologies, and, more importantly, formulated measurement criteria to compare them. Consequently, groups can be dynamically organized with respect to the similarities among the personal contexts. Individual users can engage in complex collaborations related to multiple semantics. In this paper, we want to discuss the experimental results collected from a collaborative information searching system based on the proposed context synchronization.