Semantic-web–Enhanced Hybrid Learning for Career Planning: Ontology-driven Matching, Sequence Forecasting, and Closed-loop Optimization

Abstract

Conventional counselling workflows struggle with the scale and heterogeneity of labor-market data. This manuscript presents a semantic-web–enhanced hybrid learning framework for university career planning, embedding ontology-driven modelling and knowledge-graph representation into AI-based recommendation. The framework (i) constructs a domain ontology to organize skills, roles, and behavioral features, (ii) applies natural language processing to curate and semantically align heterogeneous resources, (iii) integrates a gradient-boosted decision tree for skill-to-role matching with a transformer-based sequence model for progression forecasting, and (iv) employs a closed-loop optimization that updates ontology weights and model parameters from longitudinal outcomes. An interpretable recommendation interface provides semantic rationales to support counsellor–student dialogue, while governance measures incorporate privacy-by-design and role-based access control. In deployment with 800 final-year students, the system improved first-round interview hit rate by 27% and six-month job satisfaction by 22% compared with a matched control cohort. Ablation confirms the complementary value of structured academic records and unstructured behavioral logs. Results indicate that ontology-driven hybrid learning enables scalable, explainable, and evidence-based career guidance.