A Survey of Question Semantic Parsing for Knowledge Base Question Answering

被引：0
|
作者：
Qiu Y.-Q. [1 ,2 ]
Wang Y.-Z. [1 ,3 ]
Bai L. [2 ,4 ]
Yin Z.-Y. [1 ]
Shen H.-W. [1 ,2 ]
Bai S. [1 ]
机构：
[1] Research Center for Data Intelligence Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
[2] School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing
[3] Big Data Academy, Zhongke, Henan, Zhengzhou
[4] CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2022年 / 50卷 / 09期
关键词：
knowledge base; low resource; meaning representation; question answering; semantic parsing;
D O I：
10.12263/DZXB.20220212
中图分类号：
学科分类号：
摘要：
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. © 2022 Chinese Institute of Electronics. All rights reserved.
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页码：2242 / 2264
页数：22
相关论文
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