A Computational Framework for Skill Gap Analysis and Employability Measurement: Integrating Labour Market Data, Skills Taxonomies and Artificial Intelligence

This preprint presents a computational framework for analysing employability through skill gap analysis. The study introduces the Skill Gap Algorithm, a model that measures the alignment between individual competencies and occupational skill requirements using vector similarity metrics and labour market data.

The framework integrates natural language processing techniques for skill extraction, structured skill taxonomies such as the European Skills, Competences, Qualifications and Occupations (ESCO) classification, and vector similarity methods to quantify the distance between professional skill profiles and occupational requirements.

By incorporating labour market trend weighting, the model captures the dynamic evolution of skill demand and proposes a quantitative approach to measuring employability.

The framework contributes to the emerging field of data-driven career guidance systems capable of identifying skill gaps and generating personalised reskilling pathways aligned with labour market needs.

The framework is aligned with emerging platforms that apply skill-based analysis to career guidance and employability assessment, such as https://www.skillcoach.io/