Understanding expert finding systems: domains and techniques

Abstract

Expert finding can be required for a variety of purposes: finding referees for a conference paper, recommending consultants for a software project, and identifying qualified answerers for a question in online knowledge-sharing communities, to name a few. This paper presents taxonomy of the task of expert finding that highlights the differences between finding experts, from the type of expertise indicator’s point of view. The taxonomy supports deep understanding of different sources of expertise information in the enterprise or online communities; for example, authored documents, emails, online posts, and social networks. In addition, different content and non-content features that characterize the evidence of expertise are discussed. The goal is to guide researchers who seek to conduct studies regarding the different types of expertise indicators and state-of-the-art techniques for expert finding in organizations or online communities. The paper concludes that although researchers have utilized a large number of graph and machine-learning techniques for locating expertise, there are still technical issues associated with the implementation of some of these methods. It also corroborates that combining content-based expertise indicators and social relationships has the benefit of alleviating some of the issues related to identifying and ranking answer experts. The above findings give implications for developing new techniques for expert finding that can overcome the technical issues associated with the performance of current methods.