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This paper proposes CODER: contrastive learning on knowledge graphs forcross-lingual medical term representation. CODER is designed for medical termnormalization by providing close vector representations for different termsthat represent the same or similar medical concepts with cross-lingual support.We train CODER via contrastive learning on a medical knowledge graph (KG) namedthe Unified Medical Language System, where similarities are calculatedutilizing both terms and relation triplets from KG. Training with relationsinjects medical knowledge into embeddings and aims to provide potentiallybetter machine learning features. We evaluate CODER in zero-shot termnormalization, semantic similarity, and relation classification benchmarks,which show that CODERoutperforms various state-of-the-art biomedical wordembedding, concept embeddings, and contextual embeddings. Our codes and modelsare available at https://github.com/GanjinZero/CODER.

CODER: Knowledge infused cross-lingual medical term embedding for term normalization