Semantic Mapping of Soft Skills to the Standard EN 16234 Using Natural Language Processing

<p><span lang="EN-GB">The effective alignment of soft skills with established competency frameworks is crucial for workforce development, career guidance, and organizational talent management. This paper presents a semantic mapping approach to align soft skills from the SkillsMatch framework with the contents of the e-Competence Framework (European standard EN 16234) using Natural Language Processing (NLP) as e-CF does not explicitly illustrate those skills in the text of the standard. A calculation with cosine similarity provides indication of 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 bridge the gap between abstract soft skills and technical competencies, otherwise unfeasible in terms of effort and time due to huge amount of information to be analysed . The result is an addition that enriches and extends the standard EN16234 to offer pragmatic addition to practitioners for the application in real contexts. Moreover, the proposed method underscores the importance of integrating advanced processing tools to work with competence frameworks and models to overcome the unfeasible amount of effort and inconsistency proneness of manual methods of analysis.</span></p>