  The graph structure of biomedical data differs from those in typical
knowledge graph benchmark tasks. A particular property of biomedical data is
the presence of long-range dependencies, which can be captured by patterns
described as logical rules. We propose a novel method that combines these rules
with a neural multi-hop reasoning approach that uses reinforcement learning. We
conduct an empirical study based on the real-world task of drug repurposing by
formulating this task as a link prediction problem. We apply our method to the
biomedical knowledge graph Hetionet and show that our approach outperforms
several baseline methods.
