  Pretrained language models have shown success in many natural language
processing tasks. Many works explore incorporating knowledge into language
models. In the biomedical domain, experts have taken decades of effort on
building large-scale knowledge bases. For example, the Unified Medical Language
System (UMLS) contains millions of entities with their synonyms and defines
hundreds of relations among entities. Leveraging this knowledge can benefit a
variety of downstream tasks such as named entity recognition and relation
extraction. To this end, we propose KeBioLM, a biomedical pretrained language
model that explicitly leverages knowledge from the UMLS knowledge bases.
Specifically, we extract entities from PubMed abstracts and link them to UMLS.
We then train a knowledge-aware language model that firstly applies a text-only
encoding layer to learn entity representation and applies a text-entity fusion
encoding to aggregate entity representation. Besides, we add two training
objectives as entity detection and entity linking. Experiments on the named
entity recognition and relation extraction from the BLURB benchmark demonstrate
the effectiveness of our approach. Further analysis on a collected probing
dataset shows that our model has better ability to model medical knowledge.
