  Despite the widespread success of self-supervised learning via masked
language models (MLM), accurately capturing fine-grained semantic relationships
in the biomedical domain remains a challenge. This is of paramount importance
for entity-level tasks such as entity linking where the ability to model entity
relations (especially synonymy) is pivotal. To address this challenge, we
propose SapBERT, a pretraining scheme that self-aligns the representation space
of biomedical entities. We design a scalable metric learning framework that can
leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts.
In contrast with previous pipeline-based hybrid systems, SapBERT offers an
elegant one-model-for-all solution to the problem of medical entity linking
(MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking
datasets. In the scientific domain, we achieve SOTA even without task-specific
supervision. With substantial improvement over various domain-specific
pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining
scheme proves to be both effective and robust.
