Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts’ micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles