<cite>Existing RL-based methods</cite> for KG completion do not capture the entity's neighborhood information. Previous studies on one-shot fact prediction have shown that the local neighborhood structure improves the fact prediction performance for long-tailed relations [31, 37] . We propose a graph neural network (GNN) [9] to encode the neighborhood information of the entities and leverage the state representation with the type and neighborhood information of the entities.