ROP predicts intermediate entities and updates the entity/relation embeddings and other parameters in each step; the training signals can come from the in-path target entities and from the reasoning task specific annotations.
rOP makes use of the in-path structures to learn good quality entity/relation embeddings, which are further employed and finetuned to solve the reasoning problem.