  Infusing factual knowledge into pre-trained models is fundamental for many
knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions
(MoP), an infusion approach that can handle a very large knowledge graph (KG)
by partitioning it into smaller sub-graphs and infusing their specific
knowledge into various BERT models using lightweight adapters. To leverage the
overall factual knowledge for a target task, these sub-graph adapters are
further fine-tuned along with the underlying BERT through a mixture layer. We
evaluate our MoP with three biomedical BERTs (SciBERT, BioBERT, PubmedBERT) on
six downstream tasks (inc. NLI, QA, Classification), and the results show that
our MoP consistently enhances the underlying BERTs in task performance, and
achieves new SOTA performances on five evaluated datasets.
