BRDIA: Bidirectional Reasoning with Dynamic Instruction Adjustment for Multi-hop KGQA

Abstract

Multi-hop Knowledge Graph Question Answering (KGQA) involves retrieving entities from knowledge graph (KG) to answer users’ queries. More studies focus on generating instructions by decoding questions to guide path searches on the KG. However, as messages propagate from seed nodes to answer nodes, node embedding predominantly reflects information from forward reasoning paths, without fully considering the complete KG context. Besides, due to relation frequency differences within KG, reasoning may overly rely on common but less informative relations, while ignoring less frequent but more helpful ones. To solve these issues, we propose BRDIA, a GNN-based model that deeply integrates bidirectional reasoning. By introducing a gating mechanism, BRDIA dynamically adjusts bidirectional instructions to build reliable contextual information, better aligning KG with the query. Additionally, we introduce relation frequency to measure relation importance, ensuring that multi-hop reasoning considers both path semantics and prior topological information from the KG. We evaluated BRDIA on two major multi-hop KGQA benchmark datasets. Experimental results demonstrate the effectiveness of our proposed method.