NLP and Text mining for Enriching IT Professional Skills Frameworks

The European e-Competence Framework (e-CF) and the European Skills, Competences, Qualifications and Occupations (ESCO) classification are two key initiatives developed by the European Commission to support skills transparency, mobility, and interoperability across labour and education systems. While e-CF defines essential competences for ICT professionals through a structured framework, it provides only a limited number of illustrative skills and knowledge examples for each competence. In contrast, ESCO offers a rich, multilingual taxonomy of skills and knowledge, each accompanied by detailed descriptions, alternative labels, and links to relevant occupations. This paper explores the possibility of enriching the e-CF framework by linking it to relevant ESCO ICT skills using text embedding (MPNet) and cosine similarity. Through this approach, we demonstrate that instead of offering just 4–10 brief examples per competence, e-CF can be extended to include 15–25 semantically aligned skills and knowledge items, each with full descriptions officially translated into all EU languages. This significantly improves the clarity, usability, and interpretability of e-CF competences for various stakeholders. Furthermore, since ESCO terminology serves as the foundation for labour market analysis across the EU, establishing this linkage provides a valuable bridge between the e-CF competence model and real-time labour market intelligence, a connection that is currently lacking. The results of this study offer practical insights into how semantic technologies can enhance and align European digital skills frameworks.