电子学报 ›› 2022, Vol. 50 ›› Issue (9): 2242-2264.DOI: 10.12263/DZXB.20220212
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
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:
作者简介:
基金资助:
QIU Yun-qi1,2, WANG Yuan-zhuo1,3(), BAI Long2,4, YIN Zhi-yi1, SHEN Hua-wei1,2, BAI Shuo1
Received:
2022-03-02
Revised:
2022-05-11
Online:
2022-09-25
Published:
2022-10-26
Corresponding author:
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.
中图分类号:
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.
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[ | Freebase | 917 | 有 | 人工撰写 | |
WebQuestions[ | Freebase | 5 810 | 有 | 最终答案 | Google Suggest API |
SimpleQuestions[ | Freebase | 108 442 | 无 | 三元组 | 人工撰写 |
WebQuestionsSP[ | Freebase | 4 737 | 有 | SPARQL | 其他数据集 |
ComplexQuestions[ | Freebase | 2 100 | 有 | 最终答案 | 其他数据集、人工撰写 |
GraphQuestions[ | Freebase | 5 166 | 有 | SPARQL | 模板填充+人工复述 |
ComplexQuestions[ | Freebase | 150 | 有 | 最终答案 | WikiAnswers |
30M Factoid Questions[ | Freebase | 30 000 000 | 无 | 三元组 | 神经模型生成 |
PathQuestion[ | Freebase | 9 731 | 有 | 三元组 | 模板填充 |
CFQ[ | Freebase | 239 357 | 有 | SPARQL | 规则构造 |
GrailQA[ | Freebase | 64 331 | 有 | SPARQL | 模板填充+人工复述 |
ComplexWebQuestions[ | 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[ | DBpedia | 5 000 | 有 | SPARQL | 模板填充+人工复述 |
LC-QuAD 2.0[ | DBpedia & Wikidata | 30 000 | 有 | SPARQL | 模板填充+人工复述 |
CSQA[ | Wikidata | 1 600 000 | 有 | 最终答案 | 人工撰写 |
KQA Pro[ | Wikidata | 117 970 | 有 | SPARQL | 模板填充 |
MetaQA[ | 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[ | Freebase | 917 | 有 | 人工撰写 | |
WebQuestions[ | Freebase | 5 810 | 有 | 最终答案 | Google Suggest API |
SimpleQuestions[ | Freebase | 108 442 | 无 | 三元组 | 人工撰写 |
WebQuestionsSP[ | Freebase | 4 737 | 有 | SPARQL | 其他数据集 |
ComplexQuestions[ | Freebase | 2 100 | 有 | 最终答案 | 其他数据集、人工撰写 |
GraphQuestions[ | Freebase | 5 166 | 有 | SPARQL | 模板填充+人工复述 |
ComplexQuestions[ | Freebase | 150 | 有 | 最终答案 | WikiAnswers |
30M Factoid Questions[ | Freebase | 30 000 000 | 无 | 三元组 | 神经模型生成 |
PathQuestion[ | Freebase | 9 731 | 有 | 三元组 | 模板填充 |
CFQ[ | Freebase | 239 357 | 有 | SPARQL | 规则构造 |
GrailQA[ | Freebase | 64 331 | 有 | SPARQL | 模板填充+人工复述 |
ComplexWebQuestions[ | 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[ | DBpedia | 5 000 | 有 | SPARQL | 模板填充+人工复述 |
LC-QuAD 2.0[ | DBpedia & Wikidata | 30 000 | 有 | SPARQL | 模板填充+人工复述 |
CSQA[ | Wikidata | 1 600 000 | 有 | 最终答案 | 人工撰写 |
KQA Pro[ | Wikidata | 117 970 | 有 | SPARQL | 模板填充 |
MetaQA[ | WikiMovies | 407 513 | 有 | 最终答案 | 模板填充 |
语义表征分类 | 语义表征名称 | 主要特点 |
---|---|---|
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 | ||
在 | ||
图状逻辑表达式 | 语义图[ | 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[ | ||
抽象语义表征[ | ||
查询图[ | 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[ | 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[ | 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[ | ||
Lisp[ | 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[ |
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 |
---|---|---|
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 | ||
在 | ||
图状逻辑表达式 | 语义图[ | 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[ | ||
抽象语义表征[ | ||
查询图[ | 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[ | 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[ | 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[ | ||
Lisp[ | 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[ |
枚举方式 | 相关文献 | 主要特点 | |
---|---|---|---|
依赖特定文法 | 文献[ | 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[ | 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[ | 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[ | 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 |
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[ | 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[ | 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[ | 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[ | 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 |
方法分类 | 相关文献 | 主要特点 |
---|---|---|
单次交互 | 文献[ | 知识库查询开销小,不需要与知识库多次交互; 解码阶段无知识库指导,搜索空间大 |
迭代交互 | 文献[ | 知识库查询开销大,需要与知识库多次交互; 解码阶段与知识库交互缩减搜索空间,指导语义表征子式生成 |
表4 基于编码解码的语义解析方法分类
方法分类 | 相关文献 | 主要特点 |
---|---|---|
单次交互 | 文献[ | 知识库查询开销小,不需要与知识库多次交互; 解码阶段无知识库指导,搜索空间大 |
迭代交互 | 文献[ | 知识库查询开销大,需要与知识库多次交互; 解码阶段与知识库交互缩减搜索空间,指导语义表征子式生成 |
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