Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops and predicting the intermediate entity within the reasoning path. During the reasoning process of these methods, the representation of relations are fixed but the initial relation representation may not be optimal. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation, which undermines the ability to capture information of relations in KGs. To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities. Then we iteratively do primal entity graph reasoning, dual relation graph information propagation, and interaction between these two graphs. In this way, the interaction between entity and relation is enhanced, and we derive better entity and relation representations. Experiments on two public datasets, WebQSP and CWQ, show that our approach achieves a significant performance gain over the prior state-of-the-art. Our code is available on \url{https://github.com/yanmenxue/RAH-KBQA}.

翻译：多跳知识库问答(KBQA)旨在通过知识图谱(KG)中的多步推理找到答案实体。现有基于检索的方法通过聚焦不同跳数上的特定关系并预测推理路径中的中间实体来解决该任务。在这些方法的推理过程中，关系的表示是固定的，但初始关系表示可能并非最优。我们指出这些方法未能利用头尾实体信息以及关系之间的语义联系来增强当前关系表示，这削弱了其在知识图谱中捕获关系信息的能力。为解决该问题，我们构建了一个**对偶关系图**，其中每个节点表示原始知识图谱（**原始实体图**）中的关系，并在共享相同头实体或尾实体的关系之间建立边。随后我们迭代执行原始实体图推理、对偶关系图信息传播以及这两个图之间的交互。通过这种方式，实体与关系之间的交互得以增强，从而获得更优的实体和关系表示。在两个公开数据集WebQSP和CWQ上的实验表明，我们的方法相比现有最优方法取得了显著的性能提升。我们的代码已开源至 \url{https://github.com/yanmenxue/RAH-KBQA}。