Survey of Question Answering Based on Knowledge Graph Reasoning

doi:10.3778/j.issn.1673-9418.2111033

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1727-1741.DOI: 10.3778/j.issn.1673-9418.2111033

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Survey of Question Answering Based on Knowledge Graph Reasoning

SA Rina¹, LI Yanling¹, LIN Min¹

1. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
2. Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory, Hohhot 010015, China

Received:2021-11-04 Revised:2022-02-18 Online:2022-08-01 Published:2022-08-19
About author:SA Rina, born in 1998, M.S. candidate. Her research interests include natural language processing, spoken language understanding and know-ledge graph question answering.
LI Yanling, born in 1978, Ph.D., professor, member of CCF. Her research interests include natural language processing, spoken language understanding, machine learning, etc.
LIN Min, born in 1969, Ph.D., professor, member of CCF. His research interests include natural language processing, text mining, etc.
Supported by:
the National Natural Science Foundation of China(61806103);the National Natural Science Foundation of China(61562068);the Project of Youth Innovation and Entrepreneurship Talents of Inner Mongolia “Grassland Talents”, the Open Project Foundation of Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory(IMDBD2020013);the Science and Technology Plan Project of Inner Mongolia Autonomous Region(JH20180175);the Innovation Fund for Postgraduates of Inner Mongolia Normal University(CXJJS20127);the Scientific and Technological Research Projects of Colleges and Universities in Inner Mongolia Autonomous Region(NJZY21578);the Scientific and Technological Research Projects of Colleges and Universities in Inner Mongolia Autonomous Region(NJZY21551)

知识图谱推理问答研究综述

萨日娜¹, 李艳玲¹, 林民¹

1. 内蒙古师范大学计算机科学技术学院，呼和浩特 010022
2. 内蒙古纪检监察大数据实验室，呼和浩特 010015

作者简介:萨日娜（1998—），女，内蒙古二连浩特人，硕士研究生，主要研究方向为自然语言处理、口语理解、知识图谱问答。
李艳玲（1978—），女，内蒙古呼和浩特人，博士，教授，CCF会员，主要研究方向为自然语言处理、口语理解、机器学习等。
林民（1969—），男，内蒙古呼和浩特人，博士，教授，CCF会员，主要研究方向为自然语言处理、文本挖掘等。
基金资助:
国家自然科学基金(61806103);国家自然科学基金(61562068);内蒙古自治区“草原英才”工程青年创新创业人才项目;内蒙古纪检监察大数据实验室开放课题基金(IMDBD2020013);内蒙古自治区科技计划项目(JH20180175);内蒙古师范大学研究生创新基金(CXJJS20127);内蒙古自治区高等学校科学技术研究项目(NJZY21578);内蒙古自治区高等学校科学技术研究项目(NJZY21551)

Abstract

Abstract:

Knowledge graph question answering (KGQA) is based on analysis and understanding of questions and knowledge graph (KG) to obtain the answers. However, due to the complexity of natural language questions and the incompleteness of KG, the accuracy of answers can not be improved effectively. The knowledge graph reasoning technology can infer the missing entities in the KG and the implied relations between entities. Therefore, its application in KGQA can further improve the accuracy of answer prediction. In recent years, with the development of KGQA datasets and flexible application of knowledge graph reasoning technology, the development of the KGQA is greatly promoted. In this paper, question answering based on knowledge graph reasoning is summarized from three aspects. Firstly, this paper gives a brief overview of question answering based on knowledge graph reasoning, and introduces its challenges and related datasets. Secondly, this paper introduces the application of knowledge graph reasoning in open domain question answering, commonsense question answering and temporary knowledge question answering, and analyzes the advantages and disadvantages of each method. The open domain question answering methods are further summarized as graph embedding methods, deep learning methods and logic methods. Finally, this paper summarizes the work and prospects the future research in view of the current problems of question answering based on knowledge graph reasoning.

Key words: intelligent questions and answers, knowledge graph reasoning, knowledge graph question answering (KGQA)

摘要：

知识图谱问答（KGQA）通过对问题进行分析理解，结合知识图谱（KG）获取答案。但因自然语言问题的复杂性以及知识图谱的不完整性，答案准确率得不到有效提升。而知识图谱推理技术可以推断出知识图谱中缺失的实体以及实体间隐含的关系，因此，将知识图谱推理技术应用于知识图谱问答中可以进一步提升答案预测的准确性。近年来，知识图谱问答数据集的提出以及知识图谱推理技术的灵活应用，极大地推动了知识图谱问答的发展。对知识图谱推理问答从三方面进行归纳总结：首先对知识图谱推理问答进行了简要概述，并介绍了其面临的挑战以及相关数据集;其次对知识图谱推理在开放域问答、常识问答以及时序知识问答中的应用分别进行介绍，并分析了各问答方法的优劣，其中开放域问答方法进一步归纳为基于图嵌入的方法、基于深度学习的方法、基于逻辑的方法;最后总结工作，并根据当前知识图谱推理问答存在的问题对未来研究进行展望。

关键词: 智能问答, 知识图谱推理, 知识图谱问答（KGQA）

CLC Number:

TP183

SA Rina, LI Yanling, LIN Min. Survey of Question Answering Based on Knowledge Graph Reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1727-1741.

萨日娜, 李艳玲, 林民. 知识图谱推理问答研究综述[J]. 计算机科学与探索, 2022, 16(8): 1727-1741.

Figures/Tables 9

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数据集	发表时间	规模	是否有答案	时序	简单	复杂	常识
Free917^[20]	2013	917			√	√
WebQuestions^[21]	2013	5 810	√		√	√
SimpleQuestions^[22]	2015	100 000	√		√
WebQuestionsSP^[23]	2016	4 737	√		√	√
ComplexQuestions^[7]	2016	2 100	√			√
LC-QuAD1.0^[24]	2017	5 000			√	√
CWQ^[25]	2018	35 000	√		√	√
OpenBookQA^[29]	2018	6 000	√		√	√	√
TempQuestions^[33]	2018	1 271	√	√	√	√
LC-QuAD2.0^[26]	2019	30 000			√	√
CommonsenseQA^[30]	2019	12 000	√		√	√	√
SocialIQA^[31]	2019	38 000	√		√	√	√
RIDDLESENSE1^[32]	2021	5 700	√		√	√	√
TimeQuestions^[34]	2021	16 181	√	√	√	√
CRONQUESTIONS^[35]	2021	410 000	√	√	√	√

数据集	发表时间	规模	是否有答案	时序	简单	复杂	常识
Free917^[20]	2013	917			√	√
WebQuestions^[21]	2013	5 810	√		√	√
SimpleQuestions^[22]	2015	100 000	√		√
WebQuestionsSP^[23]	2016	4 737	√		√	√
ComplexQuestions^[7]	2016	2 100	√			√
LC-QuAD1.0^[24]	2017	5 000			√	√
CWQ^[25]	2018	35 000	√		√	√
OpenBookQA^[29]	2018	6 000	√		√	√	√
TempQuestions^[33]	2018	1 271	√	√	√	√
LC-QuAD2.0^[26]	2019	30 000			√	√
CommonsenseQA^[30]	2019	12 000	√		√	√	√
SocialIQA^[31]	2019	38 000	√		√	√	√
RIDDLESENSE1^[32]	2021	5 700	√		√	√	√
TimeQuestions^[34]	2021	16 181	√	√	√	√
CRONQUESTIONS^[35]	2021	410 000	√	√	√	√

方法	描述	优点	缺点
基于图嵌入的方法	通过将问题与KG嵌入至低维向量空间，并在此向量空间中进行运算，得到答案实体	解决知识图谱信息缺失问题	可解释性较差
基于深度学习的方法	利用神经网络对子图或关系路径建模，从而推理得到答案	有效挖掘问题与知识图谱三元组之间的语义联系	搜索空间大
基于逻辑的方法	将问句或查询转化为紧密联系KG的逻辑形式，在KG中获取答案实体	可解释性强，准确性高，能够解决逻辑判断问题	易受稀疏知识图谱影响

方法	描述	优点	缺点
基于图嵌入的方法	通过将问题与KG嵌入至低维向量空间，并在此向量空间中进行运算，得到答案实体	解决知识图谱信息缺失问题	可解释性较差
基于深度学习的方法	利用神经网络对子图或关系路径建模，从而推理得到答案	有效挖掘问题与知识图谱三元组之间的语义联系	搜索空间大
基于逻辑的方法	将问句或查询转化为紧密联系KG的逻辑形式，在KG中获取答案实体	可解释性强，准确性高，能够解决逻辑判断问题	易受稀疏知识图谱影响

模型	准确率/%	创新点
KagNet^[81]	58.9	提出GCN+LSTM+HPA框架
MHGRN^[82]	75.4	通过扩展R-GCN直接编码路径
QA-GNN^[83]	76.1	将QA作为节点加入到子图
PG^[84]	75.6	生成问题相关路径进行推理
HGN^[80]	77.3	通过对子图补全，生成包含有用事实并排除无用事实的KG子图

Survey of Question Answering Based on Knowledge Graph Reasoning

知识图谱推理问答研究综述

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模型	时间	创新点	数据集	评价标准	数值/%
TEQUILA^[33]	2018	利用规则将复杂问题分解为简单问题，并加入了时间约束	TempQuestions	F1	36.7
Exaqt^[34]	2021	微调BERT并加入时间信息，以构建问题相关子图，其次扩展R-GCN对子图进行推理	TimeQuestions	Hits@5	66.4
CRONKGQA^[35]	2021	通过改进EmbedKGQA方法，使其适用于时序KG，并提出了大型时序图谱以及相关数据集	CRONQUESTIONS	Hits@1	64.7