## 3 Results for the Research Questions
### RQ5: What Are the Datasets That Have Been Used for Expert Finding Systems?
Additionally, CiteSeer and DBLP offer the information of computer scientific literatures on main journals and conference proceedings. DBLP is the most commonly used data set in expert finding systems research (see Table 9). CiteSeer has developed a new version, CiteSeerX, which aims to provide more resources such as algorithms and metadata [33; 125]. CiteSeer test collection provide huge sources of experts and their associated documents for additional research and test data collection. It has been used by references [47; 66]. Additionally reference, [88] combined CiteSeer and Twitter test collections. They extracted the title, abstract and citation relationship from CiteSeer and user ID, posts, reading and commenting information from Twitter. The authors of reference [47] used different data sets, namely: DBLP, Microsoft \"Academic Search\", ACM Digital Library, Google Scholar, Springer's Digital Library, Google Maps API, CiteSeerX, IEEE Xplore, Academia.edu, and Facebook to combine data from different data sources. Reference [47] used DBLP to retrieve individuals' information, publications and venues. These data are incomplete because the information about expertise areas is ambiguous and the data does not hold much information about the individuals and the relationships between them (or publications). Therefore, they expanded the information about individuals and developed a set of wrappers to retrieve the required data from Academia.edu and Facebook (they are public (and academic) social networks). Facebook requires data owner authorization to extract the publicly available and private data. The study extracted information from academic indexing services, namely Microsoft Academic Search and Google Scholar (web search engines for academic publications), to identify the research interests and the individual's affiliations. In order to identify the geographic locations of the retrieved organizations, they used the Google Maps API. Also, the study extracted additional information such as citation counts, abstracts, and topics from different research databases, namely ACM Digital Library, Springer's Digital Library, CiteSeerX, and IEEE Xplore. In addition to the previous test collections, the authors in reference [6] presented a novel test collection called Lattes Expertise Retrieval (LExR). It was developed to offer a unified benchmark for researchers for expert finding and profiling in academia. Data has also been extracted from the Wikipedia data set. It is a document repository that holds articles, and each article only defines a single topic. In each article, the title is a concise phrase called a concept. In Wikipedia, categories are organized in a hierarchical structure, and this make it a good ontology to be used for extracting semantic correlations between diverse concepts [56]. Additionally, question and answer websites such as Stack Overflow, Yahoo! Answer, and Ask Me Help Desk are used to extract data sets for expert finding systems. The authors of references [16; 19; 83] used Stack Overflow (which currently contains 16 million questions 2019), while reference [17] used Yahoo! Answer. Ask Me Help Desk has been used by the authors of reference [85]. Ask Me Help Desk contains more than 500,000 questions and more than 2.5 million answers. It was ranked as one of the best Top 13 Sites for answering questions. Social networks have been widely used in expert finding systems as data sets to extract information for finding experts. In addition to Twitter, Facebook, and Academia.edu, Scholarmate has been used by the authors of references [20; 22; 25]. In addition to the well-known data sets shown in Table 9, there are other data sets that have been used for expert finding systems. For example, reference [53] used all papers published for the Pacific Asia Conference on Information Systems from 2007 to 2009, while the authors of reference [60] used the publications from the Journal of Universal Computer Science to identify experts.