## 1 Introduction  
In the past few years, several organisations have realized that effective management of all data assets could help them survive in the competitive business environment. Expertise is an important knowledge asset that is often stored in people's mind and is therefore difficult to codify. Expertise can be shared when people interconnect with one another. Users need to consult an expert to determine ways to solve their problems in a particular domain, such as medical problems. Although there is a huge volume of data available for solving problems, people still seek the services and guidance of an expert. For example, conference planners search for paper reviewers, and students require appropriate supervisors for their research [1]. The term 'expertise' is defined in psychology as the human cognitive skills that can be developed by conducting a task frequently [2]. Generally, people who display expertise in a specific field are known as experts. However, there is no unified definition of the term 'expert'. The authors in reference [3] defined an expert as an individual who has skills in his domain of expertise and can evaluate a particular domain topic. Furthermore, the authors in reference [4] identified an expert as an individual who has a great deal of knowledge and displays comprehensive skills in a specific area. The authors in reference [5] identified an expert as a person who has the best experience about a particular topic in a particular field of study, based on a collection of various factors. The expertise seekers are those who seek knowledge for specific purposes such as problem-solving, collaboration, and team formation. They require comprehensive information regarding experts who can answer their questions. It is not practical to manually collect information regarding experts, specifically in widely distributed and large-scale organisations [6; 7]. Therefore, Information Retrieval (IR) techniques have been used to facilitate the retrieval of the appropriate experts automatically. Such techniques are called expert locating systems (also known as expert finding systems or expertise retrieval). Expert finding systems are IR systems that identify candidate experts and ranking them based on their expertise in a given subject. The candidate's expertise is extracted from their expertise evidences such as publications, reports, projects, and social network [8; 9]. The expertise retrieval system is useful for individuals and organisations, as it allows them to retrieve suitable experts. Hence, these systems help the expertise seeker to find the most appropriate expert, based on the expert's actual knowledge [10]. Early expert finding systems developed to focus on specific document types. For instance, the authors in references [11; 12] studied locating experts in the technical and support departments of a software corporation. The authors in reference [13] developed a tool called Expertise Browser for locating expertise in a collaborative software engineering environment. Because of clear limitations in the above systems (i.e., concentrating on specific document types), academia and industry have given greater attention to systems that have capabilities to mine heterogeneous sources of expertise evidence accessible in an organization. The P@noptic system proposed by the authors in reference [14] is one of the first approaches of this kind of system. The P@noptic system confirmed the feasibility of expertise retrieval on heterogeneous collections. As a result, at the Text Retrieval Conference (TREC) from 2005 to 2008, an expert finding task was launched as a portion of the Enterprise Track. It provided expert finding systems researchers with a public platform to evaluate approaches and algorithms developed for expert finding. Consequently, expert finding received significant attention from the IR research community, and quick progress was made in modeling, algorithms design, and evaluation.