  Off-the-shelf biomedical embeddings obtained from the recently released
various pre-trained language models (such as BERT, XLNET) have demonstrated
state-of-the-art results (in terms of accuracy) for the various natural
language understanding tasks (NLU) in the biomedical domain. Relation
Classification (RC) falls into one of the most critical tasks. In this paper,
we explore how to incorporate domain knowledge of the biomedical entities (such
as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for
predicting Drug-Drug Interaction from textual corpus. We propose a new method,
BERTKG-DDI, to combine drug embeddings obtained from its interaction with other
biomedical entities along with domain-specific BioBERT embedding-based RC
architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly
indicate that this strategy improves other baselines architectures by 4.1%
macro F1-score.
