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

Authors:
Jason J. Jung
Affiliations:
Department of Computer Engineering, Yeungnam University, Gyeongsan, Korea 712-749
Venue:
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Year:
2009

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Abstract

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.