Expertise profiling in evolving knowledge-curation platforms

Expertise modeling has been the subject of extensive research in two main disciplines: Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA approaches 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, through micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. In this paper we present a comprehensive, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. We showcase its application in the biomedical domain and analyze its performance using two manually created datasets