## 6 Conclusions  
In this study, we have provided an overview of various expert finding systems concepts. In order to understand these systems, we determined our specific scope by formulating five different research questions. 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. This review studied research papers published between 2010 and 2019. After employing multiple processes, we selected 96 studies that focused on expert finding systems and expertise seeking. The other studies were eliminated from this review as they could not satisfy the various inclusion criteria and did not display the desired quality. This study identified research methodologies that were used in the studies. The majority of these studies involved a quantitative method (94%), followed by review (4%), and qualitative and mixed methods (1%, each). Also, this study presented different domains that have applied expert finding systems for different purposes. After data analysis, all selected studies were developed in six domains, with a majority in the academia domain (64%). Other domains, like enterprise, online KSCs, social networks, online forums, and the medical domain, had less attention. Additionally, this study described the expertise sources that have been used for expert finding systems, which were divided into three main categories--textual sources, social networks, and hybrid expertise sources. Moreover, it presented the methods that have been used for models building and placed these into six categories--generative probabilistic models, discriminative probabilistic models, voting models, network-based models, hybrid methods, and other models. Also, the datasets that have been used for evaluating expert finding systems were discussed; they were mainly used in three environments--enterprises, academia, and social networks. Moreover, the differences between expertise retrieval and expertise seeking were discussed, and the contextual factors and theories that have been integrated into expertise retrieval were identified. Finally, gaps in the current studies were identified.  
The contributions of this study are (1) it identifies different domains of expert finding systems, (2) it differentiates between expertise seeking and retrieval, (3) it identifies the contextual factors that have been combined to expert finding systems to improve their effectiveness, (4) it describes the state-of-the-art methods and datasets in expert finding systems, and (5) it identifies five main research gaps for future researchers in the expert finding systems domain. In conclusion, after reviewing all the existing studies, we state that this effort would be very helpful for academics, practitioners, and developers of expert finding systems. Expertise retrieval systems have garnered a lot of attention from researchers, and this review could help them identify the gaps and loopholes in this area of research and achieve an overview of existing studies to design their own research.  
Conceptualization, O.H.; methodology, O.H. and R.A.A.; validation, O.H., N.S., and R.A.A.; formal analysis, O.H.; investigation, O.H. and N.S.; resources, O.H., N.S., R.A.A., S.A., and A.H.; data curation, O.H.; writing--original draft preparation, O.H.; writing--review and editing, O.H., N.S., R.A.A., S.A., and A.H.; supervision, N.S. and R.A.A.  
This paper funded by Prof. Dr. [PERSON], [PERSON] and Prof. Dr. [PERSON] Universiti Teknologi Malaysia High Impact Research Grant (Vote # QJ130000.2455.04G78)).  
We would like to thank [PERSON] for his valuable inputs and contribution to this paper. We also acknowledge Ministry of Higher Education, Sudan, and University of Khartoum, Faculty of Mathematical Science, Sudan, for sponsoring [PERSON] in her PhD Program at Universiti Teknologi Malaysia.  
The authors declare no conflict of interest.