Semantic Parsing Graph Query Model for On-Demand Aggregation

doi:10.3969/j.issn.0372-2112.2020.04.018

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Semantic Parsing Graph Query Model for On-Demand Aggregation

更新时间：2025-07-08

- Semantic Parsing Graph Query Model for On-Demand Aggregation
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 763-771(2020)
- 作者机构：
  
  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
  6. 重庆大学计算机学院,重庆,400044
  7. 重庆大学土木工程学院,重庆,400044
  8. 绍兴文理学院计算机科学与工程系,浙江,绍兴,312000
  9. 重庆电子工程职业学院,重庆,401331
  10. 重庆西信天元数据资讯有限公司,重庆,401121
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.04.018
  CLC： TP302.1
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
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Semantic Parsing Graph Query Model for On-Demand Aggregation[J]. Acta Electronica Sinica, 2020, 48(4): 763-771.
DOI：

Semantic Parsing Graph Query Model for On-Demand Aggregation[J]. Acta Electronica Sinica, 2020, 48(4): 763-771. DOI： 10.3969/j.issn.0372-2112.2020.04.018.

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.

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.

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