## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?
#### Others  
Expert search is closely related to document search and other IR retrieval tasks; both traditional and modern IR methods have been applied to expert search, such as fuzzy logic, the vector space model, cluster-based retrieval, and big data. The authors in reference [74] used fuzzy logic in expert finding systems. In their model, they created fuzzy relevance profiles using TF-IDF analysis. They combined three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document to select the most relevant documents and experts related to the query topic. The unified index was used to select the most relevant documents and experts related to the query. The profiles integrated by fuzzy rule-based summarization. Furthermore, reference [66] proposed model based on Support Vector Machine and they used Random Forests for author names disambiguated. The authors in reference [61] developed two algorithms in Java to calculate the best distance for their model. They studied the problem of finding a team of experts with required skills and who have the minimal communication cost from a social network. Moreover, reference [15] adopted the fuzzy linguistic method to build the expert profile and used the fuzzy text classifier to get the relevant degree of the document to each area of knowledge. Reference [45] developed an expert finding system based on the Dempster-Shafer theory. This theory was used to combine different expertise sources. Also, the authors of references [5; 51] used web mining methods to propose multifaceted web mining heuristic tool to find people of desired expertise and to recommend scholars, respectively. Also, the authors of references [62; 63] used facility location analysis to propose an optimization framework to find an optimal group of experts for a given multi-aspect task/project. In addition to that, the authors in reference [85] proposed a novel algorithm based on the techniques provided by the WordNet dictionary to find experts in forums communities. The techniques used are text mining techniques and semantic similarity function. Additionally, reference [81] proposed a context adaptive algorithm to find the optimal expert for patients with a specific context. Furthermore, K-Nearest-Neighbor was used by the authors in reference [36] to propose a new approach called DREX to develop expert finding. Also, ontology has been used for expert finding systems. This is a formal explicit description of classes in a particular area with set of properties to describe the various features and attributes. For example, the authors of references [50; 78] developed ontological models for expert finding systems. Moreover, recommendation systems techniques have been used for expert finding. For example, the authors in reference [59] proposed a content-based recommender system to select reviewers to evaluate research proposals or articles. They developed a comprehensive algorithmic framework based on different IR techniques. Also, reference [83] proposed a recommendation algorithm called collaborative expert recommendation (CER) for expert finding based on collaborative filtering. On the other hand, big data techniques and tools have also been used for expert finding systems. For instance, reference [39] used big data to adopt an intelligent recommendation method to select appropriate experts for peer review in online research community. The authors of reference [22] proposed a social network-empowered research analytics framework (RAF) to select research projects and reviewers. The authors of reference [20] developed a novel model based on big data analytics with MapReduce to recommend experts in scientific communities.