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GTR:在跨域数据库系统中具有过渡表示的SQL生成器.

Shaojie Qiao, Chenxu Liu, Guoping Yang

    IEEE transactions on neural networks and learning systems
    |September 6, 2023
    PubMed
    概括

    本研究介绍了GTR,这是一个用于将自然语言 (NL) 转换为SQL查询的新系统. GTR使用过渡表示 (TR) 桥梁来提高跨域数据库系统的准确性.

    科学领域:

    • 数据库管理数据库管理
    • 人工智能的人工智能
    • 自然语言处理自然语言处理.

    背景情况:

    • 使用自然语言 (NL) 从数据库中自动检索数据对于自主系统至关重要.
    • 现有的NL-to-SQL技术与NL-SQL不匹配和域外单词作斗争,阻碍了准确的查询生成.
    • 由于这些复杂性,端到端的NL到SQL转换仍然具有挑战性.

    研究的目的:

    • 提出GTR,一个使用过渡表示 (TR) 的自动SQL生成器,用于在跨域数据库系统中改进NL-to-SQL转换.
    • 解决现有方法在处理NL-SQL表达式不匹配和域外词汇方面的局限性.
    • 为了提高从自然语言问题生成SQL查询的准确性和效率.

    主要方法:

    • GTR采用了三步过程:学习NL-DB模式关系,使用基于语法模型合成TR,并从TR中预测SQL.
    • 过渡表示作为一个中间"桥梁",使NL表达式与SQL实现细节保持一致.
    • 在WikiSQL和蜘蛛数据集上进行了实验,以评估GTR的性能.

    主要成果:

    • 在蜘蛛数据集上,GTR实现了58.32%的精确匹配准确度.
    • 在WikiSQL数据集上,GTR实现了71.29%的精确匹配准确度.

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  • 拟议的GTR方法在两个数据集上都超过了现有的最先进的方法.

    • 结论:

    • 通过利用过渡表示,GTR系统有效地从自然语言生成SQL查询.
    • 使用TR显著提高了NL到SQL转换的准确性,特别是在跨域情景中.
    • GTR代表了自主数据库系统和可访问数据查询的重大进步.