  Biomedical knowledge graphs permit an integrative computational approach to
reasoning about biological systems. The nature of biological data leads to a
graph structure that differs from those typically encountered in benchmarking
datasets. To understand the implications this may have on the performance of
reasoning algorithms, we conduct an empirical study based on the real-world
task of drug repurposing. We formulate this task as a link prediction problem
where both compounds and diseases correspond to entities in a knowledge graph.
To overcome apparent weaknesses of existing algorithms, we propose a new
method, PoLo, that combines policy-guided walks based on reinforcement learning
with logical rules. These rules are integrated into the algorithm by using a
novel reward function. We apply our method to Hetionet, which integrates
biomedical information from 29 prominent bioinformatics databases. Our
experiments show that our approach outperforms several state-of-the-art methods
for link prediction while providing interpretability.
