Semantic Synergy: Unlocking Policy Insights and Learning Pathways Through Advanced Skill Mapping

This research introduces a comprehensive system based on state-of-the-art natural language processing, semantic embedding, and efficient search techniques for retrieving similarities and thus generating actionable insights out of raw textual information. The system works on automatically extracting and aggregating normalized competencies out of multiple documents like policy files and curricula vitae and making strong relationships between recognized competencies, occupation profiles, and related learning courses. To validate its performance, we conducted a multi-tier evaluation that included both explicit and implicit skill references in synthetic and real-world documents. The results showed near-human-level accuracy, with F1 scores exceeding 0.95 for explicit skill detection and above 0.93 for implicit mentions. The system thereby establishes a sound foundation for supporting in-depth collaboration across the AE4RIA network. The methodology involves a multiple-stage pipeline based on extensive preprocessing and data cleaning, semantic embedding and segmentation via SentenceTransformer, and skill extraction using a FAISS-based search method. The extracted skills are associated with occupation frameworks as formulated in 1 the ESCO ontology and learning paths as training programs in the Sustainable Development Goals Academy. Moreover, interactive visualization software, implemented based on Dash and Plotly, presents interactive graphs and tables for real-time exploration and informed decision-making for involved parties in policymaking, training and learning supply, career transitions, and recruitment opportunities. Overall, the system outlined in this paper—supported by rigorous validation—presents promising prospects for better policy-making, human resource improvement, and lifelong learning based on providing structured and actionable insights out of raw, complex textual information.