A multilingual heterogeneous knowledge graph framework for curriculum-vacancy-MOOC alignment: edge-weight regression with heterogeneous graph transformers for educational recommendation
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Sandugash Serikbayeva1, Madina Sambetbayeva1, Valiya Ramazanova1,2
1Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan.
Introduction:
Digitalisation and artificial intelligence have widened the gap between competencies certified by university curricula and skills demanded by employers. MOOCs could close this gap, but existing recommenders rarely connect what a programme teaches, what the market requires, and what online courses develop within a single semantic structure.
Methods:
We built a multilingual heterogeneous knowledge graph (KG) that integrates university programmes, IT vacancies from hh.kz, and Coursera MOOCs in English, Russian, and Kazakh. A property-graph ontology was formalised and instantiated automatically. Skills were aligned with a fine-tuned multilingual Sentence Transformer (paraphrase-multilingual-mpnet-base-v2), and agglomerative clustering formed skill communities. Recommendation was cast as edge-weight regression over a competency-to-course relation and solved with GraphSAGE, HAN, and HGT encoders.
Results:
On a held-out 20% test graph, HGT achieved the best ranking quality (NDCG@10 = 0.714; with short meta-paths NDCG@10 = 0.987, RMSE = 0.108), outperforming GraphSAGE and HAN. Applied to the "Information Systems" programme of a Kazakhstani university, the system expanded the market-aligned competency profile by +36.2% with five top-ranked courses and by +170% with the full catalogue.
Discussion:
Edge-weight regression on a cross-domain heterogeneous KG produces front-loaded, actionable course recommendations for curriculum alignment. All datasets, models, graph snapshots, and code are released for reproducibility.
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