一种依需聚合的语义解析图查询模型

doi:10.3969/j.issn.0372-2112.2020.04.018

电子学报 ›› 2020, Vol. 48 ›› Issue (4): 763-771.DOI: 10.3969/j.issn.0372-2112.2020.04.018

一种依需聚合的语义解析图查询模型

李青¹, 钟将¹, 李立力², 李琪³, 张淑芳⁴, 张剑⁵

1. 重庆大学计算机学院, 重庆 400044;
2. 重庆大学土木工程学院, 重庆 400044;
3. 绍兴文理学院计算机科学与工程系, 浙江绍兴 312000;
4. 重庆电子工程职业学院, 重庆 401331;
5. 重庆西信天元数据资讯有限公司, 重庆 401121

收稿日期:2018-12-17 修回日期:2019-11-18 出版日期:2020-04-25
- 通讯作者:
- 钟将
- 作者简介:
- 李青女.1989年5月出生,陕西西安人.现为重庆大学博士生,研究主要研究方向为自然语言处理、复杂事件检测、医学信息学.E-mail:liqing@cqu.edu.cn;李立力 男.1989年出生,陕西铜川人.现为重庆大学博士生,研究主要研究方向为桥梁健康监测、数据挖掘研究.E-mail:lilili@cqu.edu.cn;李琪男.1987年出生,江苏盱眙人.博士,现为绍兴文理学院讲师,主要研究方向为图计算、数据挖掘.E-mail:liqi0713@foxmail.com;张淑芳 女.1972年出生,陕西人澄城人.博士,现为重庆电子工程职业学院副教授,主要研究方向为图数据挖掘、高性能计算.E-mail:roseymen2000@foxmail.com;张剑男.1972年出生.高级工程师,硕士,现为重庆西信天元数据资讯有限公司总经理.主要研究方向为自然语言处理、数据挖掘.E-mail:13608341660@139.com
- 基金资助:
- 国家重点研发计划 (No.2017YFB1402401）; 中央高校研究生科研创新项目 (No.2018CDYJSY0055）; 重庆市研究生科研创新项目 (No.CYB18058）; 陕西省教育厅科学技术研究计划 (No.18JK1130）; 重庆市技术创新与应用示范项目 (No.cstc2018jszx-cyzdX0086）; 重庆市技术创新与应用发展重点项目 (No.cstc2019jscx-fxyd0142）; 重庆市社会事业与民生保障科技创新专项 (No.cstc2017shmsA0641）; 重庆市教育委员会科学技术研究计划青年项目 (No.KJQN201903112）

Semantic Parsing Graph Query Model for On-Demand Aggregation

LI Qing¹, ZHONG Jiang¹, LI Li-li², LI Qi³, ZHANG Shu-fang⁴, ZHANG Jian⁵

1. College of Computer Science, Chongqing University, Chongqing 400044, China;
2. School of Civil Engineering, Chongqing University, Chongqing 400044, China;
3. Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang 312000, China;
4. Chongqing College of Electronic Engineering, Chongqing 401331, China;
5. Chongqing Xixintianyuan Data Information Co., Ltd., Chongqing 401121, China

Received:2018-12-17 Revised:2019-11-18 Online:2020-04-25 Published:2020-04-25

摘要/Abstract

摘要： 本文设计并实现了依需聚合的语义深层网查询模型——SemtoSql+.提出以长短期记忆网络为基础，采用词嵌入技术将语料库训练为模型输入的词向量；并结合依赖关系图，将SQL语句四个层级的生成问题转换为依赖关系图中槽的填充问题，同时引入注意力机制有效避免了传统模型中的顺序问题；采用随机蒙蔽机制，构建依需聚合的增强型SemtoSql+模型.

关键词: 自然语义处理, 复杂事件, 语义网, 深度学习

Abstract: In this paper,we design and propose SemtoSql+,a semantic deep network query model based on demand aggregation.At the same time,it is a network to address the complex and cross-domain Text-to-SQL generation task.Based on LSTM and Word2Vec embedding technology,the corpus is trained as the input word vector of the model.Combined with the dependency graph method,the problem of SQL statement generation transforms into slot filling.SemtoSql+divides complex tasks into four levels and constructs by the need of aggregation,using the attention mechanism to effectively avoid the order problem in the traditional model and using a random masked mechanism to enhance the model.

Key words: natural semantic processing, complex events, semantic web, deep learning

中图分类号:

TP302.1

李青, 钟将, 李立力, 李琪, 张淑芳, 张剑. 一种依需聚合的语义解析图查询模型[J]. 电子学报, 2020, 48(4): 763-771.

LI Qing, ZHONG Jiang, LI Li-li, LI Qi, ZHANG Shu-fang, ZHANG Jian. Semantic Parsing Graph Query Model for On-Demand Aggregation[J]. Acta Electronica Sinica, 2020, 48(4): 763-771.

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一种依需聚合的语义解析图查询模型

Semantic Parsing Graph Query Model for On-Demand Aggregation

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