Multi-hop question answering of bridge inspection by adopting knowledge graph embedding technology

Abstract

The complexity of relationships between bridge inspection presents a significant challenge for the answer system. Sparse knowledge graph (KG) due to limited triples further compounds the issue. To overcome this, this paper proposes a dynamic reasoning strategy for bridge inspection question answering. The framework comprises a teacher-student network and dynamic reasoning strategy. The teacher network, based on the neural state machine (NSM), acquires auxiliary intermediate supervision signals. Its output provides probability distribution and entity embedding as input for the student network. The student network, also NSM-based, provides accurate answers with the aid of intermediate supervision signals. To handle incomplete KG, the dynamic reasoning strategy incorporates knowledge embedding, updating the KG by capturing contextual information of each related entity node in relation to the question. Experiments on the bridge inspection dataset demonstrated the effectiveness of this method, outperforming other approaches.