  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}.
