## 4 Research Findings on Identified Gaps  
This section presents research gaps and future work derived from the results of research questions and discussion presented in Section 4. This paper identifies the following gabs for future researchers in expert finding systems. 1. Academia domains versus others--Table 3 shows that, from the six main domain categories in this study, the majority of the selected papers (65%) used expert finding systems in academia, which is developed for different tasks like finding research collaborators, paper reviewers, supervisors, similar experts, group of experts, and university-industry collaborators. Although using expert finding systems in academia is promising, integrating these tasks into one comprehensive system for the whole university is important. This system can assist universities in managing their knowledge assets, which is important for innovation. It also assists universities in finding the gap in specific areas, can support expert seekers by providing them to find an expert based on their knowledge, and can encourage them to seek knowledge and help. Additionally, it can assist the experts themselves by appreciating their efforts when they help people and share their knowledge; this leads to an increase in their reputation and widens their networks. Expert finding systems research have not attracted much attention in other domains. For example, in medicine, it can be used to find pharmacists, medical laboratory staff, dentists, and other medical staff. Developing these systems will help people all over the world to find an expert for their specific needs at any time. 2. Expertise sources availability--According to the critical analysis of the selected studies, the authors found that the availability of expertise sources is a main challenge in the current studies of expert finding systems due to the following issues: 1. Information privacy--System developers and organization owners must clearly identify the benefits of their systems. If users perceive value from expert finding systems, they will use these systems and supply the required personal information. Additionally, privacy-preserving techniques should be developed, and users should be given fair control over the storage and usage of their information [75]. 2. Data integration--In general, integration of different expertise sources is a challenging task, and there are four problems in data integration need more effort from researchers in the future, which are the following: (1) nicknaming, referring to the concept of different names in multiple networks for the same person; (2) name ambiguity, referring to individuals in social networks that have very similar or exactly the same name and a name may refer to different people; (3) multiple references, or finding more than one profile for the same person in a network; and (4) local access to profiles, as sometimes it is difficult to access the whole profiles in a network simultaneously. Thus, there is a need for crawling methods to access the profiles. 3. Information updateness and completeness--integrating several data sources is very important in order to guarantee the completeness of expertise-related evidence. For example, in the academia domain, various expertise sources could be used, such as homepages, publications, research descriptions, course descriptions, social networks, project/grant repositories, supervised student theses, citation indexes, and movies. This information can be collected from staff homepages if the homepages are updated. A lot of this information has a shorter lifetime, and incomplete data can lead to wrong decisions. Thus, there is a lack of research for developing motivation frameworks for organizations to motivate their employees to update their profiles regularly. 3. Expert finding systems methods--there is a need for developing sophisticated algorithms to integrate several expertise related evidences.