Towards automating the recruitment process

Business world is getting increasingly dynamic. Information processing using knowledge-, service-, and cloud-based systems makes the use of complex, dynamic and often knowledge-intensive activities an inevitable task. Knowledge-intensive processes contain a set of coordinated tasks and activities, controlled by knowledge workers to achieve a business objective or goal. Talent acquisition and recruitment processes - i.e., the process of identifying the jobs vacancy, analyzing the job requirements, reviewing applications, screening, shortlisting and selecting the right candidate - are example of Knowledge-intensive processes. Attracting and recruiting right talent is a key differentiator in modern organizations. In this thesis, we analyze the state of the art in traditional recruitment model and identify the main gaps when evaluating candidate profile with position description. We put the first step towards automating the recruitment process. We present a framework and algorithms to: imitate the knowledge of recruiters into the domain knowledge, extract data and knowledge from business artifacts, e.g., candidates' CV and position description, and link them to the facts in the domain Knowledge Base. We develop a digital dashboard to help recruiters draw insights in a quick and easy way. We adopt a motivating scenario in recruiting a Data Scientist role in an organization, and conduct a user study to highlight how iRecruit significantly facilitates the knowledge intensive tasks in the recruitment process.