面向知识库问答的问句语义解析研究综述

doi:10.12263/DZXB.20220212

电子学报 ›› 2022, Vol. 50 ›› Issue (9): 2242-2264.DOI: 10.12263/DZXB.20220212

面向知识库问答的问句语义解析研究综述

仇韫琦¹^,², 王元卓¹^,³(), 白龙²^,⁴, 尹芷仪¹, 沈华伟¹^,², 白硕¹

^1.中国科学院计算技术研究所数据智能系统研究中心，北京 100190
^2.中国科学院大学计算机科学与技术学院，北京 101408
^3.中科大数据研究院，河南郑州 450046
^4.中国科学院计算技术研究所网络数据科学与技术重点实验室，北京 100190

收稿日期:2022-03-02 修回日期:2022-05-11 出版日期:2022-09-25
- 通讯作者:
- 王元卓
- 作者简介:
- 仇韫琦男，1994年出生.中国科学院计算技术研究所博士研究生.主要研究方向为知识库问答、语义解析.E-mail: qiuyunqi19b@ict.ac.cn
  王元卓男，1978年出生.博士.中国科学院计算技术研究所研究员，博士生导师，中科大数据研究院院长.主要研究方向为网络大数据分析、开放知识计算、社交网络演化计算.
  白龙男，1993年出生.中国科学院计算技术研究所博士研究生.主要研究方向为知识图谱、事件预测.E-mail: bailong18b@ict.ac.cn
  尹芷仪女，1982年出生.博士.中国科学院计算技术研究所高级工程师，硕士生导师.主要研究方向为社会计算、网络空间安全.E-mail: yinzhiyi@ict.ac.cn
  沈华伟男，1982年出生.博士.中国科学院计算技术研究所研究员，博士生导师，数据智能系统研究中心主任.主要研究方向为网络数据挖掘、社交网络分析、图神经网络.E-mail: shenhuawei@ict.ac.cn
  白硕男，1956年出生.博士.中国科学院计算技术研究所研究员，博士生导师，恒生电子股份有限公司首席科学家.E-mail: bshuo@sina.cn
- 基金资助:
- 国家自然科学基金 (62172393); 中原英才计划-中原科技创新领军人才项目资助 (204200510002)

A Survey of Question Semantic Parsing for Knowledge Base Question Answering

QIU Yun-qi¹^,², WANG Yuan-zhuo¹^,³(), BAI Long²^,⁴, YIN Zhi-yi¹, SHEN Hua-wei¹^,², BAI Shuo¹

^1.Research Center for Data Intelligence Systems，Institute of Computing Technology，Chinese Academy of Sciences，Beijing 100190，China
^2.School of Computer Science and Technology，University of Chinese Academy of Sciences，Beijing 101408，China
^3.Big Data Academy，Zhongke，Zhengzhou，Henan 450046，China
^4.CAS Key Laboratory of Network Data Science and Technology，Institute of Computing Technology，Chinese Academy of Sciences，Beijing 100190，China

Received:2022-03-02 Revised:2022-05-11 Online:2022-09-25 Published:2022-10-26
- Corresponding author:
- WANG Yuan-zhuo

摘要/Abstract

摘要：

知识库问答（Knowledge Base Question Answering，KBQA）借助知识库中精度高、关联性强的结构化知识，为给定的复杂事实型问句提供准确、简短的答案.语义解析是知识库问答的主流方法之一，该类方法在给定的问句语义表征形式下，将非结构化的问句映射为结构化的语义表征，再将其改写为知识库查询获取答案.目前，面向知识库问答的语义解析方法主要面临三个挑战：首先是如何选择合适的语义表征形式以表达问句的语义，然后是如何解析问句的复杂语义并输出相应的语义表征，最后是如何应对特定领域中数据标注成本高昂、高质量数据匮乏的问题.本文从上述挑战出发，分析了知识库问答中常用的语义表征的特点与不足，然后梳理现有方法并总结分析其如何应对问句的复杂语义，接着介绍了当前方法在标注数据匮乏的低资源场景下的尝试，最后展望并讨论了面向知识库问答的语义解析的未来发展方向.

关键词: 知识库, 问答, 语义表征, 语义解析, 低资源

Abstract:

Knowledge base question answering(KBQA) provides accurate and short answers to complex factoid questions with the help of high-precision and highly relevant structured knowledge in the knowledge base(KB). Semantic parsing has become one of the mainstream methods of KBQA. Under the given form of question meaning representation, this kind of method maps unstructured questions into structured meaning representations, and then rewrites them as KB queries to obtain answers. At present, semantic parsing for KBQA mainly faces three challenges: first how to choose a suitable meaning representation form to express the semantics of questions, then how to parse the complex semantics of questions and output the corresponding meaning representations, and finally how to deal with the high cost of labeling datasets and the lack of annotated data in specific domains. Starting from the above challenges, this paper first analyzed the characteristics and shortcomings of meaning representations commonly used in KBQA and then combed out how existing methods deal with the complex semantics of questions. After that, this paper introduced the current attempts in low-resource scenarios and finally discussed the future directions of semantic parsing for KBQA.

Key words: knowledge base, question answering, meaning representation, semantic parsing, low resource

中图分类号:

TP391

仇韫琦, 王元卓, 白龙, 尹芷仪, 沈华伟, 白硕. 面向知识库问答的问句语义解析研究综述[J]. 电子学报, 2022, 50(9): 2242-2264.

QIU Yun-qi, WANG Yuan-zhuo, BAI Long, YIN Zhi-yi, SHEN Hua-wei, BAI Shuo. A Survey of Question Semantic Parsing for Knowledge Base Question Answering[J]. Acta Electronica Sinica, 2022, 50(9): 2242-2264.

图/表 16

图1 图数据库示意图

图2 面向知识库问答的语义解析的一般流程

图3 面向知识库问答的语义解析方法分类

表1 以图数据库为基准知识库的部分英文知识库问答数据集

数据集名称	基准知识库	样例规模	含有复杂问句	问句标注形式	问句来源
Free917^[30]	Freebase	917	有	$λ$ 演算	人工撰写
WebQuestions^[2]	Freebase	5 810	有	最终答案	Google Suggest API
SimpleQuestions^[31]	Freebase	108 442	无	三元组	人工撰写
WebQuestionsSP^[32]	Freebase	4 737	有	SPARQL	其他数据集
ComplexQuestions^[33]	Freebase	2 100	有	最终答案	其他数据集、人工撰写
GraphQuestions^[36]	Freebase	5 166	有	SPARQL	模板填充+人工复述
ComplexQuestions^[34]	Freebase	150	有	最终答案	WikiAnswers
30M Factoid Questions^[37]	Freebase	30 000 000	无	三元组	神经模型生成
PathQuestion^[25]	Freebase	9 731	有	三元组	模板填充
CFQ^[38]	Freebase	239 357	有	SPARQL	规则构造
GrailQA^[39]	Freebase	64 331	有	SPARQL	模板填充+人工复述
ComplexWebQuestions^[40]	Freebase	34 689	有	SPARQL	模板填充+人工复述
QALD series(http://qald.aksw.org/)	DBpedia	50～558	有	SPARQL	人工撰写
LC-QuAD^[41]	DBpedia	5 000	有	SPARQL	模板填充+人工复述
LC-QuAD 2.0^[42]	DBpedia & Wikidata	30 000	有	SPARQL	模板填充+人工复述
CSQA^[35]	Wikidata	1 600 000	有	最终答案	人工撰写
KQA Pro^[43]	Wikidata	117 970	有	SPARQL	模板填充
MetaQA^[44]	WikiMovies	407 513	有	最终答案	模板填充

表1 以图数据库为基准知识库的部分英文知识库问答数据集

数据集名称	基准知识库	样例规模	含有复杂问句	问句标注形式	问句来源
Free917^[30]	Freebase	917	有	$λ$ 演算	人工撰写
WebQuestions^[2]	Freebase	5 810	有	最终答案	Google Suggest API
SimpleQuestions^[31]	Freebase	108 442	无	三元组	人工撰写
WebQuestionsSP^[32]	Freebase	4 737	有	SPARQL	其他数据集
ComplexQuestions^[33]	Freebase	2 100	有	最终答案	其他数据集、人工撰写
GraphQuestions^[36]	Freebase	5 166	有	SPARQL	模板填充+人工复述
ComplexQuestions^[34]	Freebase	150	有	最终答案	WikiAnswers
30M Factoid Questions^[37]	Freebase	30 000 000	无	三元组	神经模型生成
PathQuestion^[25]	Freebase	9 731	有	三元组	模板填充
CFQ^[38]	Freebase	239 357	有	SPARQL	规则构造
GrailQA^[39]	Freebase	64 331	有	SPARQL	模板填充+人工复述
ComplexWebQuestions^[40]	Freebase	34 689	有	SPARQL	模板填充+人工复述
QALD series(http://qald.aksw.org/)	DBpedia	50～558	有	SPARQL	人工撰写
LC-QuAD^[41]	DBpedia	5 000	有	SPARQL	模板填充+人工复述
LC-QuAD 2.0^[42]	DBpedia & Wikidata	30 000	有	SPARQL	模板填充+人工复述
CSQA^[35]	Wikidata	1 600 000	有	最终答案	人工撰写
KQA Pro^[43]	Wikidata	117 970	有	SPARQL	模板填充
MetaQA^[44]	WikiMovies	407 513	有	最终答案	模板填充

表2 知识库问答中常用的语义表征

语义表征分类	语义表征名称	主要特点
$λ$ 演算及其衍生	$λ$ 演算^[16,17]	图灵完备,具有很强的表达完备性,但表达较为繁琐,可读性差,构造较难
$λ$ 演算及其衍生	$λ$ -DCS^[45]	在 $λ$ 演算基础上隐去变量,表达更紧凑,构造难度比 $λ$ 演算小
图状逻辑表达式	语义图^[46]	基于问句句法结构,构造过程中依赖特定文法或现有句法解析工具得到无根基的语义表征;再通过自定义操作、实体关系映射等映射到知识库子图,得到有根基的语义表征,构造较为便捷,可读性强,但图结构限制了其表达完备性
	语义查询图^[47]
	抽象语义表征^[48]
	查询图^[3]	直接从知识库子图中构造,不依赖特定文法或现有解析工具,构造更便捷,可读性强,但图结构限制了其表达完备性
程序语言	SPARQL^[18]	具有较强的表达完备性,一般通过识别问句中的实体和关系完成SPARQL模板填充
	Prolog^[54]	基于一阶谓词逻辑,具有较强的语义完备性,结构相对紧凑,但无法像图状逻辑表达式一样直接利用知识库的拓扑结构信息,在生成嵌套结构时易出错
	FunQL^[56]
	Lisp^[55]	以前缀表达式列表为表达形式,可通过更新自定义函数来增强其表达能力,具有很强的表达完备性,但无法像图状逻辑表达式一样利用知识库的拓扑结构信息,在生成嵌套结构时易出错
	KoPL^[43]

表2 知识库问答中常用的语义表征

语义表征分类	语义表征名称	主要特点
$λ$ 演算及其衍生	$λ$ 演算^[16,17]	图灵完备,具有很强的表达完备性,但表达较为繁琐,可读性差,构造较难
$λ$ 演算及其衍生	$λ$ -DCS^[45]	在 $λ$ 演算基础上隐去变量,表达更紧凑,构造难度比 $λ$ 演算小
图状逻辑表达式	语义图^[46]	基于问句句法结构,构造过程中依赖特定文法或现有句法解析工具得到无根基的语义表征;再通过自定义操作、实体关系映射等映射到知识库子图,得到有根基的语义表征,构造较为便捷,可读性强,但图结构限制了其表达完备性
	语义查询图^[47]
	抽象语义表征^[48]
	查询图^[3]	直接从知识库子图中构造,不依赖特定文法或现有解析工具,构造更便捷,可读性强,但图结构限制了其表达完备性
程序语言	SPARQL^[18]	具有较强的表达完备性,一般通过识别问句中的实体和关系完成SPARQL模板填充
	Prolog^[54]	基于一阶谓词逻辑,具有较强的语义完备性,结构相对紧凑,但无法像图状逻辑表达式一样直接利用知识库的拓扑结构信息,在生成嵌套结构时易出错
	FunQL^[56]
	Lisp^[55]	以前缀表达式列表为表达形式,可通过更新自定义函数来增强其表达能力,具有很强的表达完备性,但无法像图状逻辑表达式一样利用知识库的拓扑结构信息,在生成嵌套结构时易出错
	KoPL^[43]

图4 问句“What is the name of the company which Disney acquired in 2006?”的语义图[49](a) 无根基的语义图 (b) 有根基的语义图

图5 问句“Who is starring in Spanish movies produced by Benicio del Toro?”的抽象语义表征[53]

图6 问句“Who first voiced Meg on Family Guy?”的查询图[3]

表3 基于枚举排序的语义解析方法分类

枚举方式		相关文献	主要特点
依赖特定文法		文献[1,30,58~62]	可控性强,枚举阶段依赖词汇映射与组合规则,具体应用时受限于规则,导致泛化性较差
自定义处理步骤	基于线图	文献[2,63,64]	相邻子式组合方式由自定义操作控制,泛化性较好
	基于转移	文献[3,33,46,47,49,53,65~76]	基于问句的句法结构或问句相关的知识库子图,以当前语义表征构造情况为状态,通过自定义的处理步骤,迭代生成语义表征,处理步骤可调整,可干预性较强
	基于模板填充	文献[34,77,79~87]	根据问句的语义信息直接从知识库中抽取三元组或子图,填充到候选查询模板中构成候选知识库查询.模板严格限定了最终表征的结构,模板的覆盖率决定了可回答问句类型的上限

图7 组合范畴文法解析示意[1]

图8 问句“Where was Obama born?”的解析流程示意[2]

图9 基于状态转移的查询图生成流程示意[3]

图10 预定义的3种模板[79]

表4 基于编码解码的语义解析方法分类

方法分类

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面向知识库问答的问句语义解析研究综述

A Survey of Question Semantic Parsing for Knowledge Base Question Answering

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