  We propose to use reinforcement learning to inform transformer-based
contextualized link prediction models by providing paths that are most useful
for predicting the correct answer. This is in contrast to previous approaches,
that either used reinforcement learning (RL) to directly search for the answer,
or based their prediction on limited or randomly selected context. Our
experiments on WN18RR and FB15k-237 show that contextualized link prediction
models consistently outperform RL-based answer search, and that additional
improvements (of up to 13.5% MRR) can be gained by combining RL with a link
prediction model. The PyTorch implementation of the RL agent is available at
https://github.com/marina-sp/reinform
