计算机工程2025，Vol.51Issue(9)：129-138,10.DOI:10.19678/j.issn.1000-3428.0069410
基于关系感知图神经网络的Text-to-SQL方法
Text-to-SQL Method Based on Relation-aware Graph Neural Network
摘要
Abstract
Text-to-SQL semantic parsing task aims to transform natural language problems into executable SQL statements.In recent years,many researchers have applied methods such as pre-training models to this task and have made some progress.However,because existing pre-training models are not re-trained for Text-to-SQL tasks,they cannot adapt well to the scene semantic feature information of the task,which affects the parsing performance of the models.At the same time,many methods are prone to ignoring the relationship between natural language questions and database schemes,which results in semantic ambiguities in the parsing process.To solve these problems,this paper proposes a new RGA-T5 model for Text-to-SQL semantic parsing,which introduces a relation-aware Heterogeneous Graph Neural Network(HGNN)into the pre-training model T5,constructs the input entities and relations as nodes on the heterogeneous graph,and realizes semantic relation-awareness of the input sequences of the model by applying the Graph Neural Network(GNN).Simultaneously,the method also proposes a spatial gating adapter,the parameters of which are trained to realize fine-tuning of the model so that the model can be adapted to the semantic feature information in different scenarios for this task and reduce the introduction of irrelevant information.The experimental results show that the proposed method improves the performance over other advanced Text-to-SQL parsing methods on the Spider dataset,thereby verifying the model's effectiveness.关键词
语义解析/预训练模型/异构图神经网络/空间门控单元/适配器Key words
semantic parse/pre-training model/Heterogeneous Graph Neural Network(HGNN)/spatial gating unit/adapter分类
信息技术与安全科学引用本文复制引用
曹渝昆,王天浩,李云峰,陈明,李晶晶,刘元旻..基于关系感知图神经网络的Text-to-SQL方法[J].计算机工程,2025,51(9):129-138,10.基金项目
国家自然科学基金(61802249). （61802249）
