Abstract
The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.
Highlights
In the past few years, several organisations have realized that effective management of all data assets could help them survive in the competitive business environment
The expert finding systems have been developed in different domains such as academia, enterprise, and social networks
These research questions were mainly formulated to find out the existing domains of expert finding systems, expertise sources, methods, datasets, the differences between expertise retrieval and expertise seeking, and the factors and theories that have been combined to create these systems
Summary
In the past few years, several organisations have realized that effective management of all data assets could help them survive in the competitive business environment. Expertise is an important knowledge asset that is often stored in people’s mind and is difficult to codify. Expertise can be shared when people interconnect with one another. Users need to consult an expert to determine ways to solve their problems in a particular domain, such as medical problems. There is a huge volume of data available for solving problems, people still seek the services and guidance of an expert. Conference planners search for paper reviewers, and students require appropriate supervisors for their research [1]
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