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Large language models struggle with one-shot SPARQL generation for multi-hop knowledge graph questions, but training them as agentic systems with reinforcement learning enables reliable, iterative query refinement using execution feedback. A compact 3B-parameter model trained purely via outcome-driven RL learns to recover from errors and significantly outperforms zero-shot baselines, demonstrating a scalable blueprint for teaching AI agents to use formal symbolic tools effectively.AI & Data Science · Dec 22, 2025

Agentic Reinforcement Learning for Improving Knowledge Graph Question Answering Reliability

Large language models struggle with one-shot SPARQL generation for multi-hop knowledge graph questions, but training them as agentic systems with reinforcement learning enables reliable, iterative query refinement using execution feedback. A compact 3B-parameter model trained purely via outcome-driven RL learns to recover from errors and significantly outperforms zero-shot baselines, demonstrating a scalable blueprint for teaching AI agents to use formal symbolic tools effectively.

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SPARQL-LLM: From Natural Language to Executable Knowledge Graph QueriesAI & Data Science · Dec 19, 2025

SPARQL-LLM: From Natural Language to Executable Knowledge Graph Queries

Translating natural language questions into executable SPARQL queries remains a major barrier to accessing knowledge graphs at scale. While large language models have shown promise in this area, many existing approaches such as Graph-RAG struggle with reliability, cost, and production readiness, especially when applied to complex or federated datasets. This post presents a high-level, executive-friendly overview of the SPARQL-LLM architecture published in ACM Transactions on the Web (2025).

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