融合外部知识的知识图谱问答方法研究  

Study on Knowledge Graph Question Answering Methods Incorporating External Knowledge

作　　者：白云天 郝文宁 靳大尉 刘小语 BAI Yuntian;HAO Wenning;JIN Dawei;LIU Xiaoyu(College of Command&Control Engineering,Army Engineering University of PLA,Nanjing 210014,China)

机构地区：[1]陆军工程大学指挥控制工程学院,江苏南京210014

出　　处：《软件导刊》2024年第9期56-62,共7页Software Guide

基　　金：国防工业技术发展计划项目(JCKY2020601B018)。

摘　　要：知识图谱问答是自然语言处理领域的热门研究方向之一。现有方法主要存在两大挑战:一是难以理解复杂的自然语言形式问题,二是实体表示通常只限于字面含义,缺乏深入的语义阐释。针对上述问题,提出一种融合外部知识的知识图谱问答方法DEK-KGQA。首先通过问题知识图谱子图和QA上下文构建联合图,其次利用预训练语言模型计算联合图中节点的相关性评分,最后引入外部知识,以增强问答推理过程中的信息交互和推理能力。在CommonsenseQA数据集上进行实验验证,并与现有方法进行比较。实验结果表明,该方法在常识问答任务中取得了更好的效果,验证了该方法的有效性。此外,通过消融实验验证了该方法中各个部分对整体性能的影响。Knowledge graph question answering is one of the hot research areas in the field of natural language processing.Existing methods face two main challenges:difficulty in understanding complex natural language questions and limited semantic interpretation of entity representations.To address these challenges,a knowledge graph question answering method called DEK-KGQA is proposed,which integrates external knowledge.First,a joint graph is constructed by combining the question knowledge graph subgraph and the QA context.Then,the relevance scores of nodes in the joint graph are calculated using pre-trained language models.Finally,external knowledge is introduced to enhance information interaction and reasoning ability during the question answering process.Experimental validation is conducted on the CommonsenseQA dataset,comparing the proposed method with existing methods.The results demonstrate that the proposed method achieves better performance in commonsense question answering tasks,validating its effectiveness.In addition,ablation experiments are conducted to evaluate the impact of each component on the overall performance.

关 键 词：知识图谱问答 QA上下文 预训练语言模型 外部知识 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]