## 4 Research Findings on Identified Gaps
For example, in a university, the expert profile is an important source of information due to the high quality of the data, but the data is not complete and is not usually updated. Furthermore, a social network also consists of a huge volume of data, but generally, the data quality is poorer than that of university data repositories. Hence, universities must integrate these information sources. 4. Data sets--The current data sets for expert finding systems have been developed for a particular environment--for example, UvT, TU, DBLP, CiteSeer, and LExR--were developed for academia. TREC was developed for enterprise, and Yahoo! Answer, Facebook, and Twitter datasets were developed for social networks. There is a need for research to develop data sets that combine information from academia and social networks and from enterprise and social networks because they can complement each other, and the completed benchmark data sets can encourage expert finding systems developers to develop different systems. The benchmark data sets reduce the effort needed for solving information updates problems, information completeness, data cleaning, and name ambiguity problems. Additionally, many of the data on the web (not developed as a well-known data set) are either not updated or suffer from fake entries, and sometimes developers face a lack of access to resources. Therefore, developing benchmark data sets will reduce these challenges. 5. Combining expertise retrieval and expertise seeking--Expertise retrieval systems and expertise seeking are concerned with finding experts, but expertise retrieval involves developing algorithms for identifying a set of people who possess the necessary expertise for a specific query based on all documents related to this candidate. Expertise seeking can be described as the the selection of people as a consultation source based on information needs. It is needed for further human-based knowledge. Expertise seeking helps in developing models to identify the factors that could play a vital role in an expert's selection based on a human-centred viewpoint. These factors have been called contextual factors in previous research. A majority of research in expertise retrieval systems is dependent on a system-centred perspective, which concentrates on the identification of good matches between the need for expertise and content of documents related to experts. Integrating contextual factors with expertise retrieval systems can improve their effectiveness. Recent research has tried to combine these factors with expertise retrieval systems. Based on this study, the most factors that have been combined from expertise seeking to expertise retrieval are expert-related factors such as quality and accessibility. From the 14 studies in Table 7 that combined contextual factors with expertise retrieval, 10 studies combined expert related factors and the reset combined social, organizational, motivational, and community factors. Accordingly, expert-related factors are the most important factors in expertise retrieval. A majority of the current expert finding systems focused on the global notion of expertise. Due to the difficulties noted in the determination of the accepted definition of an expert along with the heterogeneity of the expertise sources, the complete data about the expert cannot be acquired. Hence, one needs to consider a personalised view of the expertise based on user-specific information requirements. Therefore, there is a need for researchers to develop suitable models based on a theoretical foundation that can examine the manner in which contextual factors influence expert selections in every domain. In the academic domain, researchers must develop a contextual-based model for each particular task, since it can be difficult to develop a common contextual-based model for all of a university's tasks. These models can be integrated with content-based models to improve the performance of expert retrieval [16; 25; 27].