  Predicting interactions among heterogenous graph structured data has numerous
applications such as knowledge graph completion, recommendation systems and
drug discovery. Often times, the links to be predicted belong to rare types
such as the case in repurposing drugs for novel diseases. This motivates the
task of few-shot link prediction. Typically, GCNs are ill-equipped in learning
such rare link types since the relation embedding is not learned in an
inductive fashion. This paper proposes an inductive RGCN for learning
informative relation embeddings even in the few-shot learning regime. The
proposed inductive model significantly outperforms the RGCN and
state-of-the-art KGE models in few-shot learning tasks. Furthermore, we apply
our method on the drug-repurposing knowledge graph (DRKG) for discovering drugs
for Covid-19. We pose the drug discovery task as link prediction and learn
embeddings for the biological entities that partake in the DRKG. Our initial
results corroborate that several drugs used in clinical trials were identified
as possible drug candidates. The method in this paper are implemented using the
efficient deep graph learning (DGL)
