摘要
基于知识图谱的问答系统可以解析用户问题,已成为一种检索知识、自动回答所询问题的有效途径.知识图谱问答系统通常是利用神经程序归纳模型,将自然语言问题转化为逻辑形式,在知识图谱上执行该逻辑形式能够得到答案.然而,使用预训练语言模型和知识图谱的知识问答系统包含两个挑战:(1)给定问答(questionanswering, QA)上下文,需要从大型知识图谱(knowledge graph, KG)中识别相关知识;(2)对QA上下文和KG进行联合推理.基于此,提出一种语言模型驱动的知识图谱问答推理模型QA-KGNet,将QA上下文和KG连接起来形成一个工作图,使用语言模型计算给定QA上下文节点与KG节点的关联度,并使用多头图注意力网络更新节点表示.在Commonsense QA、OpenBookQA和Med QA-USMLE真实数据集上进行实验来评估QA-KGNet的性能,实验结果表明:QA-KGNet优于现有的基准模型,表现出优越的结构化推理能力.
The question-answering system based on knowledge graphs can analyze user questions,and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: (1) given the QA (question-answering) context, relevant knowledge needs to be identified from a large KG (knowledge graph);(2) it isneeded to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability.
作者
乔少杰
杨国平
于泳
韩楠
覃晓
屈露露
冉黎琼
李贺
QIAO Shao-Jie;YANG Guo-Ping;YU Yong;HAN Nan;QIN Xiao;QU Lu-Lu;RAN Li-Qiong;LI He(School of Software Engineering,Chengdu University of Information Technology,Chengdu 610225,China;Digital Media Art Key Laboratory of Sichuan Province(Sichuan Conservatory of Music),Chengdu 610021,China;School of Management,Chengdu University of Information Technology,Chengdu 610225,China;Guangxi Key Lab of Human-machine Interaction and Intelligent Decision(Nanning Normal University),Nanning 530100,China;School of Computer Science and Technology,Xidian University,Xi’an 710071,China)
出处
《软件学报》
EI
CSCD
北大核心
2023年第10期4584-4600,共17页
Journal of Software
基金
国家自然科学基金(61962006)
四川省科技计划(2021JDJQ0021,2022YFG0186)
四川音乐学院数字媒体艺术四川省重点实验室资助项目(21DMAKL02)
成都市技术创新研发项目(2021-YF05-00491-SN)
成都市重大科技创新项目(2021-YF08-00156-GX)
成都市“揭榜挂帅”科技项目(2021-JB00-00025-GX)
成都市软科学研究项目(2021-RK00-00065-ZF,2021-RK00-00066-ZF)
广西重大创新驱动项目(桂科AA22068057)
四川省社会科学高水平团队项目(2015Z177)。
关键词
知识图谱
预训练语言模型
QA上下文
多头图注意力网络
联合推理
knowledge graph(KG)
pre-trained language model
question-answering(QA)context
multi-head graph attention network
joint reasoning
