Q2Cypher: Converting Natural Language Questions to Cypher with Fine-Tuned Large Language Models

Knowledge Base Question Answering (KBQA) intends to obtain credible answers to natural language questions based on knowledge bases. Knowledge graphs, also known as graph databases, explicitly present a large amount of factual knowledge and the associations among them. Neo4j, one of the most popular graph databases, uses the Cypher Query Language (CQL) for efficient querying. Due to its complexity, converting natural language questions into CQL, i.e., Text-to-CQL, is in demand, but the performance is undesirable for existing models. To address the issue, we propose Q2Cypher, a Text-to-CQL framework that is able to generate corresponding CQL based on natural language questions by fine-tuning open-source large language models (LLMs). Q2Cypher employs an unsupervised joint retrieval method to further align entity and relation names in the CQL, thus improving generation from the models more directly. We conduct experiments on open-source LLMs such as ChatGLM2, Baichuan2 and Llama-2, using different fine-tuning methods such as LoRA, Freeze and QLoRA for comparison. The experimental results demonstrate that Q2Cypher is effective and achieves better performance than existing methods on the SpCQL dataset.