InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915594964566016 |
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| author | Jian, Xiangru Dong, Zhengyuan Özsu, M. Tamer |
| author_facet | Jian, Xiangru Dong, Zhengyuan Özsu, M. Tamer |
| contents | In recent years, querying semantic web data using SPARQL has remained challenging, especially for non-expert users, due to the language's complex syntax and the prerequisite of understanding intricate data structures. To address these challenges, we propose InteracSPARQL, an interactive SPARQL query generation and refinement system that leverages natural language explanations (NLEs) to enhance user comprehension and facilitate iterative query refinement. InteracSPARQL integrates LLMs with a rule-based approach to first produce structured explanations directly from SPARQL abstract syntax trees (ASTs), followed by LLM-based linguistic refinements. Users can interactively refine queries through direct feedback or LLM-driven self-refinement, enabling the correction of ambiguous or incorrect query components in real time. We evaluate InteracSPARQL on standard benchmarks, demonstrating significant improvements in query accuracy, explanation clarity, and overall user satisfaction compared to baseline approaches. Our experiments further highlight the effectiveness of combining rule-based methods with LLM-driven refinements to create more accessible and robust SPARQL interfaces. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_02002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations Jian, Xiangru Dong, Zhengyuan Özsu, M. Tamer Databases Artificial Intelligence Information Retrieval In recent years, querying semantic web data using SPARQL has remained challenging, especially for non-expert users, due to the language's complex syntax and the prerequisite of understanding intricate data structures. To address these challenges, we propose InteracSPARQL, an interactive SPARQL query generation and refinement system that leverages natural language explanations (NLEs) to enhance user comprehension and facilitate iterative query refinement. InteracSPARQL integrates LLMs with a rule-based approach to first produce structured explanations directly from SPARQL abstract syntax trees (ASTs), followed by LLM-based linguistic refinements. Users can interactively refine queries through direct feedback or LLM-driven self-refinement, enabling the correction of ambiguous or incorrect query components in real time. We evaluate InteracSPARQL on standard benchmarks, demonstrating significant improvements in query accuracy, explanation clarity, and overall user satisfaction compared to baseline approaches. Our experiments further highlight the effectiveness of combining rule-based methods with LLM-driven refinements to create more accessible and robust SPARQL interfaces. |
| title | InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations |
| topic | Databases Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2511.02002 |