Multi-hop Knowledge Base Question Answering Model Based on Hierarchical Structure Graph
Knowledge Base Question Answering(KBQA)aims to interpret natural language questions and obtain answer entities via retrieval and inference in a Knowledge Graph(KG).Recently,considerable attention has been given to multi-hop KBQA,which faces a significant,yet often overlooked challenge:complex questions typically contain multiple relational intentions with an inferential order that is frequently ignored.To address this challenge,this paper proposes a multi-hop KBQA model based on a Hierarchical Structure Graph(HSG-KBQA).This model is designed to recognize and guide the relational hierarchical order of natural language questions,enabling the selection of appropriate relational intents at each inference step.The proposed hierarchical structure graph explicitly represents the relational hierarchical order in questions and encodes the dependency information into the question using an LSTM-BiGCN encoding layer.Furthermore,the concept of a virtual node is introduced to learn the state of the KG during inference utilizing graph pooling techniques.Additionally,an attention mechanism-based decoder with hierarchical weights is presented,aiming to optimize instruction generation.This ensures that each hop instruction aligns more accurately with the relational chain order in the question.Experimental results show that the HSG-KBQA model demonstrates superior performance,achieving Hits@1 scores of 71.3%on the WebQuestionsSP dataset and 97.3%(PQ-2H)and 89.7%(PQ-3H)on the PathQuestions dataset.These results are superior to those of the benchmark model,indicating the enhanced capability of the proposed model in handling the KBQA task.
国家科技期刊平台
NSTL
万方数据
维普
