Relation Cross-fusion Attention Assistance Networks for Multi-hop Question Answering over Knowledge Graphs

Abstract Multi-hop knowledge graph question answering aims to find answer entities from the knowledge graph based on natural language questions. This is a challenging task as it requires precise reasoning about entity relationships at each step. When humans perform multi-hop reasoning, they usually focus on specific relations between different hops and determine the next entity. However, most algorithms often choose the wrong specific relations, causing the system to deviate from the correct reasoning path. In multi-hop question answering, the specific relation between each hop is crucial. The existing TransferNet model mainly relies on question representation for relational reasoning, but cannot accurately calculate the specific relational distribution, which will profoundly affect question answering performance. On this basis, this paper proposes an interpretable assiatance framework, which makes full use of relation embedding and question semantics, and uses the attention mechanism to cross-fuse the relevant information of them to assist in calculating the relation distribution of each hop. Extensive experiments are conducted on two English datasets, WebQSP and CWQ, demonstrating that the proposed model outperforms state-of-the-art models by a large margin.