Term clustering is important in biomedical knowledge graph construction.
Using similarities between terms embedding is helpful for term clustering.
State-of-the-art term embeddings leverage pretrained language models to encode
terms, and use synonyms and relation knowledge from knowledge graphs to guide
contrastive learning. These embeddings provide close embeddings for terms
belonging to the same concept. However, from our probing experiments, these
embeddings are not sensitive to minor textual differences which leads to
failure for biomedical term clustering. To alleviate this problem, we adjust
the sampling strategy in pretraining term embeddings by providing dynamic hard
positive and negative samples during contrastive learning to learn fine-grained
representations which result in better biomedical term clustering. We name our
proposed method as CODER++, and it has been applied in clustering biomedical
concepts in the newly released Biomedical Knowledge Graph named BIOS.

CODER++ 使用动态难样本在对比学习中提供细粒度的表示，从而在生物医学术语聚类方面取得更好的效果，应用于 BIOS 的生物医学概念聚类中。