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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).