Job Role Description and Skill Matching in a Rapidly Changing Labor Market Using Knowledge Engineering

The job market is continuously evolving and suffers from skill mismatch, while digital platforms for job seeking, human resource management and training planning are widely adopting intelligent matching engines. Efficient job description and successful skill matching require standard terminologies for job positions and their requirements/qualifications, such as the ‘European Skills, Competences, Qualifications and Occupations’ (ESCO). The aim of this paper is to search whether ESCO—as the most representative job-related information model—has been adopted in the current rapidly evolving job market and the degree in which standardized job roles and their related skillsets are in line with the content found in the current job ads. Additionally, we intend to identify possible missing elements of this framework, towards its wider adoption and advanced skill-matching recommendation systems. As a representative case, the study was focused on selected IT professions in the Greek labor market. To this end, we applied a text mining process to 400 job ads, in order to capture the skillsets required by recruiting companies. The identified requirements for the selected job roles were used to model part of the Greek IT labor market. This model was then compared with the suggested requirements of the ESCO framework. It was found that the degree of matching between the skills in ads and the skills suggested by ESCO, is notably small and that the skills frequently requested in current IT job ads that were not included in ESCO, were mostly related to recently developed technologies and to soft skills.