$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL

• URL: http://arxiv.org/abs/2311.01862v2
• Date: Mon, 1 Jul 2024 14:59:58 GMT
• Title: $R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
• Authors: Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai,
• Abstract summary: We introduce a novel approach, $R3$-NL2GQL, integrating both small and large Foundation Models for ranking, rewriting, and refining tasks. We have developed a bilingual dataset, sourced from graph database manuals and selected open-source Knowledge Graphs (KGs)
• Score: 45.13624736815995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural language to Graph Query Language (NL2GQL) encounters hurdles due to the distinct nature of GQL compared to SQL, alongside the diverse forms of GQL. Moving away from traditional rule-based and slot-filling methodologies, we introduce a novel approach, $R^3$-NL2GQL, integrating both small and large Foundation Models for ranking, rewriting, and refining tasks. This method leverages the interpretative strengths of smaller models for initial ranking and rewriting stages, while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats. Addressing the scarcity of datasets in this emerging field, we have developed a bilingual dataset, sourced from graph database manuals and selected open-source Knowledge Graphs (KGs). Our evaluation of this methodology on this dataset demonstrates its promising efficacy and robustness.

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