计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (8): 1727-1741.DOI: 10.3778/j.issn.1673-9418.2111033
萨日娜1, 李艳玲1, 林民1
收稿日期:
2021-11-04
修回日期:
2022-02-18
出版日期:
2022-08-01
发布日期:
2022-08-19
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。基金资助:
SA Rina1, LI Yanling1, LIN Min1
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.Supported by:
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。
中图分类号:
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.
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.
数据集 | 发表 时间 | 规模 | 是否有答案 | 时序 | 简单 | 复杂 | 常识 |
---|---|---|---|---|---|---|---|
Free917[ | 2013 | 917 | √ | √ | |||
WebQuestions[ | 2013 | 5 810 | √ | √ | √ | ||
SimpleQuestions[ | 2015 | 100 000 | √ | √ | |||
WebQuestionsSP[ | 2016 | 4 737 | √ | √ | √ | ||
ComplexQuestions[ | 2016 | 2 100 | √ | √ | |||
LC-QuAD1.0[ | 2017 | 5 000 | √ | √ | |||
CWQ [ | 2018 | 35 000 | √ | √ | √ | ||
OpenBookQA[ | 2018 | 6 000 | √ | √ | √ | √ | |
TempQuestions[ | 2018 | 1 271 | √ | √ | √ | √ | |
LC-QuAD2.0[ | 2019 | 30 000 | √ | √ | |||
CommonsenseQA[ | 2019 | 12 000 | √ | √ | √ | √ | |
SocialIQA[ | 2019 | 38 000 | √ | √ | √ | √ | |
RIDDLESENSE1[ | 2021 | 5 700 | √ | √ | √ | √ | |
TimeQuestions[ | 2021 | 16 181 | √ | √ | √ | √ | |
CRONQUESTIONS[ | 2021 | 410 000 | √ | √ | √ | √ |
表1 知识图谱问答数据集
Table 1 Knowledge graph based on question answering databases
数据集 | 发表 时间 | 规模 | 是否有答案 | 时序 | 简单 | 复杂 | 常识 |
---|---|---|---|---|---|---|---|
Free917[ | 2013 | 917 | √ | √ | |||
WebQuestions[ | 2013 | 5 810 | √ | √ | √ | ||
SimpleQuestions[ | 2015 | 100 000 | √ | √ | |||
WebQuestionsSP[ | 2016 | 4 737 | √ | √ | √ | ||
ComplexQuestions[ | 2016 | 2 100 | √ | √ | |||
LC-QuAD1.0[ | 2017 | 5 000 | √ | √ | |||
CWQ [ | 2018 | 35 000 | √ | √ | √ | ||
OpenBookQA[ | 2018 | 6 000 | √ | √ | √ | √ | |
TempQuestions[ | 2018 | 1 271 | √ | √ | √ | √ | |
LC-QuAD2.0[ | 2019 | 30 000 | √ | √ | |||
CommonsenseQA[ | 2019 | 12 000 | √ | √ | √ | √ | |
SocialIQA[ | 2019 | 38 000 | √ | √ | √ | √ | |
RIDDLESENSE1[ | 2021 | 5 700 | √ | √ | √ | √ | |
TimeQuestions[ | 2021 | 16 181 | √ | √ | √ | √ | |
CRONQUESTIONS[ | 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 |
表2 KGQA推理方法总结
Table 2 Summary of KGQA reasoning methods
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[ | 58.9 | 提出GCN+LSTM+HPA框架 |
MHGRN[ | 75.4 | 通过扩展R-GCN直接编码路径 |
QA-GNN[ | 76.1 | 将QA作为节点加入到子图 |
PG [ | 75.6 | 生成问题相关路径进行推理 |
HGN[ | 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 |
表3 模型在CommonsenseQA测试集上准确率及创新点
Table 3 Accuracy and innovation points of models on CommonsenseQA test set
模型 | 准确率/% | 创新点 |
---|---|---|
KagNet[ | 58.9 | 提出GCN+LSTM+HPA框架 |
MHGRN[ | 75.4 | 通过扩展R-GCN直接编码路径 |
QA-GNN[ | 76.1 | 将QA作为节点加入到子图 |
PG [ | 75.6 | 生成问题相关路径进行推理 |
HGN[ | 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 |
模型 | 时间 | 创新点 | 数据集 | 评价标准 | 数值/% |
---|---|---|---|---|---|
TEQUILA[ | 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[ | 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[ | 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 |
表4 时序知识问答方法总结
Table 4 Summary of temporal KGQA methods
模型 | 时间 | 创新点 | 数据集 | 评价标准 | 数值/% |
---|---|---|---|---|---|
TEQUILA[ | 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[ | 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[ | 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 |
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