More recent work formulates multi-hop reasoning as a sequential decision problem, and leverages reinforcement learning (RL) to perform effective path search Das et al., 2018;Shen et al., 2018;.
mINERVA (Das et al., 2018) uses the RE-INFORCE algorithm (Williams, 1992) to train an end-to-end model for multi-hop KG query answering: given a query relation and a source entity, the trained agent searches over the KG starting from the source and arrives at the candidate answers without access to any pre-computed paths.