Abstract

Aligning academic transcripts with occupational competency requirements remains challenging because course labels and job-skill terms are semantically ambiguous, role-specific, and difficult to explain. This paper proposes the Ontology Framework for Multi-level Competency Mapping (O4CM), a semi-automated framework integrating a Large Language Model (LLM) ensemble, Human-in-the-Loop (HITL) verification, Sentence-BERT (SBERT) semantic representation, the Path Consistency Index (PCI), and Total Accumulated Competency Score/Normalised Total Accumulated Competency Score (TACS/NTACS) ranking. O4CM was evaluated on a historical job-posting corpus and anonymised transcripts from five university programmes through ablation, sensitivity analysis, baseline comparison, and expert-labelled validation. The LLM ensemble reached high consensus for 21 of 22 Occupational Information Network (O*NET) knowledge-domain mappings (95.45%), each of which was subsequently expert-verified. In a computing-only expert-aligned analysis, the full framework most closely matched expert rankings across three data-domain roles. Within this dataset, ontology-path evidence can support more transparent competency ranking for educational advising and exploratory recruitment screening.

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