Large language models have made notable progress in translating natural-language questions into SPARQL queries for knowledge graphs. Recent studies from the first Text2SPARQL challenge have explored zero-shot and few-shot scenarios, as well as in-context learning techniques. With state-of-the-art (SoTA) results, the challenge exposes significant gaps, including limited use of open-source models and a scarcity of open-source fine-tuning, especially for reasoning. In this sense, we propose a new method for the Text2SPARQL task that combines pre-fine-tuning with explicit reasoning and a subsequent reward-based fine-tuning stage. First, we perform a fine-tuning to generate both a reasoning chain and the corresponding SPARQL query. Next, we apply reinforcement learning guided by reward functions. Our experiments show that the proposed model outperforms all baselines and six SoTA methods.
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