Saturday 13 September 2025
Researchers have made significant strides in developing a framework that enables large language models (LLMs) to generate SPARQL queries over scholarly knowledge graphs (SKGs). This breakthrough has the potential to revolutionize the way we interact with vast amounts of information and make it more accessible to everyone.
The team behind FIRESPARQL, a modular framework designed for SPARQL query generation, identified two primary challenges that LLMs face when attempting to generate queries over SKGs. The first challenge is structural inconsistency, where the generated queries often lack essential elements or contain redundant triples. The second challenge is semantic inaccuracy, where incorrect entities or properties are included despite a correct query structure.
To address these issues, the researchers developed a three-module architecture that incorporates fine-tuned LLMs for SPARQL generation, an optional retrieval-augmented generation (RAG) module for providing relevant context, and a lightweight SPARQL correction layer. This framework enables the LLMs to generate high-quality queries that accurately capture the nuances of the SKG.
The team evaluated FIRESPARQL on the SciQA benchmark, a dataset built on top of the Open Research Knowledge Graph (ORKG). The results show that fine-tuning the LLMs for SPARQL generation significantly improves both the syntactic quality and execution accuracy of generated queries. In fact, the best-performing configuration achieved state-of-the-art results across all evaluation metrics.
One of the key advantages of FIRESPARQL is its ability to adapt to different domains and knowledge graphs. This flexibility makes it an attractive solution for a wide range of applications, from answering complex scientific questions to providing personalized recommendations.
While there are still limitations to the framework, such as the need for high-quality context retrieval and the potential for noise in the generated queries, FIRESPARQL represents a significant step forward in the development of LLMs for SKGs. As researchers continue to refine this technology, we can expect to see even more innovative applications emerge.
The implications of FIRESPARQL are far-reaching, with the potential to transform the way we interact with information and make new discoveries. By enabling LLMs to generate high-quality SPARQL queries over SKGs, this framework opens up a world of possibilities for researchers, students, and anyone seeking to explore the vast expanse of human knowledge.
Cite this article: “Revolutionizing Information Retrieval with FIRESPARQL: A Framework for Large Language Models over Scholarly Knowledge Graphs”, The Science Archive, 2025.
Large Language Models, Scholarly Knowledge Graphs, Sparql Queries, Firesparql, Modular Framework, Retrieval-Augmented Generation, Lightweight Correction Layer, Sciqa Benchmark, Open Research Knowledge Graph, Natural Language Processing
