  Named entity disambiguation (NED), which involves mapping textual mentions to
structured entities, is particularly challenging in the medical domain due to
the presence of rare entities. Existing approaches are limited by the presence
of coarse-grained structural resources in biomedical knowledge bases as well as
the use of training datasets that provide low coverage over uncommon resources.
In this work, we address these issues by proposing a cross-domain data
integration method that transfers structural knowledge from a general text
knowledge base to the medical domain. We utilize our integration scheme to
augment structural resources and generate a large biomedical NED dataset for
pretraining. Our pretrained model with injected structural knowledge achieves
state-of-the-art performance on two benchmark medical NED datasets: MedMentions
and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57
accuracy points.
