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