R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL

Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai


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, R3-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.
Anthology ID:
2024.findings-emnlp.800
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13679–13692
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.800
DOI:
10.18653/v1/2024.findings-emnlp.800
Bibkey:
Cite (ACL):
Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, and Hongfeng Chai. 2024. R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 13679–13692, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (Zhou et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.800.pdf