## 1 Introduction
Expert finding systems have been used in different domains and environments such as enterprise [9; 15], question answering communities [16; 17], an online knowledge community [18; 19], social networks [20; 21; 22; 23; 24], and academia [25; 26; 27; 28]. Experts finding systems present the following advantages [10]:  
1. Individual Benefits--these systems, along with finding people, also link people who would otherwise not have the opportunity to meet in person. They could also increase the individual's awareness regarding the activities in the organisation and allow people to tailor their responses based on people they may meet in future. 2. Benefits for an organisation (use of conversation for retrieving tacit knowledge)--Expert finding systems help organizations in two ways: (1) They map tacit knowledge and help individuals to develop a better awareness about which individual knows what; however, information of the knowledge location alone is insufficient. The organisation benefits if this knowledge can be applied to actual problems. (2) Any expert finding system must help in building social capital by improving the connections between people who are acquainted with one another and increasing conversations between people who are not acquainted, thus extending their social network (i.e., development of weak or strong ties). The expert finding task usually has procedures that can be classified into the following five issues:  
1. Expertise evidence (sources of data) selection--Expertise evidence selection extracts the expertise-related data and information from which experts can be discovered. Data related to the person should be obtained to discover whether or not the individual is an expert in a particular field. 2. Expert representation--a key objective of expert finding systems is to provide users with information that effectively enables not only locating expertise but also deciding and selecting among relevant experts. It is necessary to identify which information can be useful in determiningamong experts whose expertise seeker is not personally familiar with them. Generally, an expert can be determined based on his documented evidence and relationships and human interaction factors (which are called contextual factors). 3. Model building--Model building consists of pre-processing, indexing, and modelling. 1. Pre-processing and indexing--constructing indexes is a regular task for document retrieval systems, and numerous open source software tools exist to facilitate this task; for expertise retrieval, however, there is a further challenge. The identifiers of a candidate expert (e.g., names, emails, etc.) need to be recognized in documents, and heterogeneous data sources need to be integrated to identify experts. Numerous text pre-processing techniques for traditional IR applications are implemented in expert search. For instance, several expert search systems eliminate stopwords from raw text document data, to achieve a higher level of precision [1]. An expert's data is commonly collected from many documents in heterogeneous data sources. This is a significant difference with document retrieval that produces technical challenges to both data integration and indexing. As an example, different names may refer to the same person or the same names in different sources might refer to different persons. Named entity recognition (NER) and disambiguation play a fundamental role in most operational expert search systems [1]. A simple way of carrying out NER is to use the candidate's identifiers as the query and associate the retrieved documents with that person [29]. Reference [30] proposed rule-based matching techniques to identify candidates by their names (using three matching forms--exact match, initial and last name, and last name only) and by their emails. 2. Modelling and retrieval--Modelling and retrieval are about building models to associate candidate experts with a user query and to provide a ranking based on the strength of these associations [1].