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Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is useddoi:10.18653/v1/d18-1362 dblp:conf/emnlp/LinSX18 fatcat:pqubglr3mzdqxaqzckqwcd3eo4