  Graph Databases (Graph DB) are widely applied in various fields, including
finance, social networks, and medicine. However, translating Natural Language
(NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to
be challenging due to its inherent complexity and specialized nature. Some
approaches have sought to utilize Large Language Models (LLMs) to address
analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a
particular domain, the absence of domain-specific NL-GQL data pairs makes it
difficult to establish alignment between LLMs and the graph DB. To address this
challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT
to create NL-GQL data pairs based on the given graph DB with self-instruct.
Then, we use the created data to fine-tune LLMs, thereby achieving alignment
between LLMs and the graph DB. Additionally, during inference, we propose a
method that extracts relevant schema to the queried NL as the input context to
guide LLMs for generating accurate GQLs.We evaluate our method on two
constructed datasets deriving from graph DBs in finance domain and medicine
domain, namely FinGQL and MediGQL. Experimental results demonstrate that our
method significantly outperforms a set of baseline methods, with improvements
of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on
EX, respectively.
