发表机构
HCMC University of Technology; University of Alberta; University of Alabama at Birmingham（胡志明市理工大学; 阿尔伯塔大学; 阿拉巴马大学伯明翰分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种LLM驱动的多智能体系统，可将RDBMS自动转换为图数据库，经实验验证其问答准确率及延迟表现均优于传统SQL方案。
AI 中文摘要
关系数据库管理系统（RDBMS）存在多项局限，包括多跳查询执行缓慢、缺乏图形化解释的可解释性。相比之下，图数据库具备更直观高效的数据模式，在大型数据集上执行速度更快。现有多数RDBMS转换管道聚焦于运行传统加载命令并依赖Cypher查询，然而利用大语言模型（LLM）生成有效图数据模式、显著降低图数据库歧义性的效率，在现有研究文献中尚未得到充分探索。本文提出一种新型算法，通过采用大语言模型驱动的新型ETL智能体，在将表和列名保存至数据集市前对其进行标准化，从而搭建RDBMS与图数据库之间的桥梁。多智能体系统在ETL智能体、分析智能体与图智能体之间生成循环讨论，通过迭代建议和评分图数据库模式的过程优化最终设计。我们确保最终图数据库在接受数据转换前满足三项标准：准确性、基于事实性与忠实性。该系统展示了一种通过全面端到端流程自动将表格数据库转换为图数据库的有效管道。我们在BFSI数据集的1081个样本上，针对简单、中等、困难三个复杂度级别，测量了转换后图数据库的效率表现。具体而言，CypherAgent在使用图数据库的问答任务中达到85.6%的准确率，比PostgreSQL类型RDBMS上SQLAgent针对所有查询的准确率高出12.12%。此外，图数据库表现出更快的性能，延迟降低约3倍。
英文摘要
RDBMS (Relational Database Management System) databases face several limitations, including slow execution with multi-hop queries and a lack of explainability by graphical interpretations. In contrast, Graph database offers a more intuitive and efficient data schema that performs faster execution on large datasets. Most existing RDBMS conversion pipelines focus on running traditional loading commands and relying on Cypher queries. However, the efficiency of using an LLM to generate an effective graph data schema, significantly reducing the ambiguity of the graph database, remains underexplored in the current research literature. This paper presents a novel algorithm that bridges RDBMS and graph database by using a novel LLM-powered ETL agent to standardize table and column names before saving them to the Data Mart. A Multi-Agent System generates a looping discussion between ETL, Analyzer, and Graph agents to optimize the final design through an iterative process of suggesting and scoring the graph database schema. We ensure that the final graph database meets three criteria before being accepted for data conversion: Accuracy, Groundedness, and Faithfulness. This system demonstrates an effective pipeline to automatically convert a tabular database into a graph database through a comprehensive end-to-end process. Our study highlights notable efficiency in using the converted graph database, which is measured on 1,081 samples of the BFSI dataset across three levels of complexity (easy, medium, and hard). Specifically, CypherAgent achieves an 85.6% accuracy for Q&A tasks using a Graph database, which is 12.12% higher than the accuracy achieved by an SQLAgent on the RDBMS database type PostgreSQL, for all queries. Additionally, the Graph database demonstrates faster performance, reducing latency by approximately 3 times.
