面向知识库问答的问句语义解析研究综述

仇韫琦; 王元卓; 白龙; 尹芷仪; 沈华伟; 白硕

doi:10.12263/DZXB.20220212

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面向知识库问答的问句语义解析研究综述

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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
- 电子学报 2022年50卷第9期页码：2242-2264
- 作者机构：
  
  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
- 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" ]
- 基金信息：
  
  国家自然科学基金(62172393;U1836206;U21B2046);中原英才计划-中原科技创新领军人才项目资助(204200510002)
- DOI：10.12263/DZXB.20220212
  中图分类号： TP391;TP182
- 收稿：2022-03-02，
  
  修回：2022-05-11，
  
  纸质出版：2022-09-25
- 稿件说明：
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仇韫琦,王元卓,白龙等.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(09):2242-2264.

QIU Yun-qi,WANG Yuan-zhuo,BAI Long,et al.A Survey of Question Semantic Parsing for Knowledge Base Question Answering[J].ACTA ELECTRONICA SINICA,2022,50(09):2242-2264.
仇韫琦,王元卓,白龙等.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(09):2242-2264. DOI： 10.12263/DZXB.20220212.

QIU Yun-qi,WANG Yuan-zhuo,BAI Long,et al.A Survey of Question Semantic Parsing for Knowledge Base Question Answering[J].ACTA ELECTRONICA SINICA,2022,50(09):2242-2264. DOI： 10.12263/DZXB.20220212.

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.

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.

关键词

Keywords

references

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