  Linking biomedical entities is an essential aspect in biomedical natural
language processing tasks, such as text mining and question answering. However,
a difficulty of linking the biomedical entities using current large language
models (LLM) trained on a general corpus is that biomedical entities are
scarcely distributed in texts and therefore have been rarely seen during
training by the LLM. At the same time, those LLMs are not aware of high level
semantic connection between different biomedical entities, which are useful in
identifying similar concepts in different textual contexts. To cope with
aforementioned problems, some recent works focused on injecting knowledge graph
information into LLMs. However, former methods either ignore the relational
knowledge of the entities or lead to catastrophic forgetting. Therefore, we
propose a novel framework to pre-train the powerful generative LLM by a corpus
synthesized from a KG. In the evaluations we are unable to confirm the benefit
of including synonym, description or relational information.
