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 used for training, the agent
can be misled by spurious search trajectories that incidentally lead to the
correct answer. We propose two modeling advances to address both issues: (1) we
reduce the impact of false negative supervision by adopting a pretrained
one-hop embedding model to estimate the reward of unobserved facts; (2) we
counter the sensitivity to spurious paths of on-policy RL by forcing the agent
to explore a diverse set of paths using randomly generated edge masks. Our
approach significantly improves over existing path-based KGQA models on several
benchmark datasets and is comparable or better than embedding-based models.

文中提出了两个方法来解决不完整知识图谱上的多跳推理查询回答中存在的问题，其一是使用预训练的一跳嵌入模型来估算未观察到事实的奖励，其二是使用随机生成的边缘掩码来强制代理探索不同的路径，该方法在多个基准数据集上明显优于现有的基于路径的 KGQA 模型，并且与基于嵌入的模型相当甚至更好。