计算机工程2025，Vol.51Issue(5)：114-123,10.DOI:10.19678/j.issn.1000-3428.0069127
面向跨域自然语言生成SQL语句的超图神经网络
Hypergraph Neural Networks for Cross-domain Text-to-SQL
摘要
Abstract
Graph Neural Network(GNN)have been widely used as encoders in recent years for cross-domain Text-to-SQL.The encoding process based on GNN substantially improves the generalization of generative models under cross-domain Text-to-SQL by capturing the association information between database schema and natural language questions.Existing GNN approaches have limitations in the heterogeneous graph structure encoding learning process,and the node-centered linking prediction of database schema and natural language questions is prone to mismatch in complex semantic scenarios.To address this issue,we propose a heterogeneous graph learning framework for cross-domain Text-to-SQL.We propose relational edge subgraph construction and edge hypergraph attention network for the edge-centered learning process of heterogeneous graphs,to effectively learn the differentiated structural features of relational edges and nodes in heterogeneous graphs,and to implement the effective generation of Structured Query Language(SQL)statements in complex semantic scenarios.To validate the effectiveness of the proposed framework,sufficient experimental comparisons are conducted on multiple cross-domain Text-to-SQL datasets.The results demonstrate that compared with the existing GNN baseline algorithms,the framework achieves significant improvement in both F1 value and Exact Matching Accuracy(EMA)metrics,and has stronger generalization in complex cross-domain scenarios.关键词
自然语言处理/自然语言生成SQL语句解析/深度学习/图构建/图神经网络Key words
Natural Language Processing(NLP)/Text-to-SQL parsing/deep learning/graph construction/Graph Neural Network(GNN)分类
计算机与自动化引用本文复制引用
郝志峰,黎阳霖,许柏炎,蔡瑞初..面向跨域自然语言生成SQL语句的超图神经网络[J].计算机工程,2025,51(5):114-123,10.基金项目
科技创新2030—"新一代人工智能"重大项目(2021ZD0111501) （2021ZD0111501）
国家优秀青年科学基金(62122022) （62122022）
国家自然科学基金(61876043,61976052,62206064). （61876043,61976052,62206064）
