Semantic mapping of soft skills from skillsmatch framework to the standard EN 16234-1 using transformer-based NLP

The effective alignment of soft skills with established competency frameworks is essential for the automated alignment of competences within workforce analytics frameworks. This paper presents a semantic mapping approach using Natural Language Processing (NLP) to align soft skills from the SkillsMatch framework with the contents of the e-Competence Framework (European standard EN 16234-1) as it does not explicitly illustrate those skills in the text. Cosine similarity index provides an indication of the semantic closeness of each soft skill to the e-CF dimensions to map them according to score thresholds, with additional confirmation by experts. This approach provides an automated, scalable solution to enable the semantic linkage between soft and technical competences, otherwise unfeasible in terms of effort and time due to the huge amount of information to be analysed. The result is additional explicit information on soft skills that enriches and extends the standard EN 16234-1 offering pragmatic guidance to practitioners for the application in real contexts. Moreover, the proposed method underscores the importance of integrating embedding-based semantic similarity modelling to work with competence frameworks and standards to extract and analyse information for additional practical value, overcoming the limitations of traditional manual methods.