Expertise Modelling in Community-driven Knowledge Curation Platforms

Expertise modelling has been the subject of extensive research in two main disciplines Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA techniques build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data, there has been a significant shift from static to evolving documents, characterized by micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide a comprehensive, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) a finegrained provenance model, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on microcontributions, and (iii) collaboration network analysis to create, refine and enrich expertise profiles in communitycentred environments, based on the relationships between networks of collaborators.

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