  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.
