Expertise Profiling in Evolving Knowledgecuration Platforms

Abstract

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.