InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations
This addresses the problem of complex SPARQL syntax and data structures for non-expert users, representing an incremental improvement through integration of existing methods.
The paper tackled the challenge of querying semantic web data with SPARQL for non-expert users by proposing InteracSPARQL, an interactive system that uses natural language explanations to refine queries, resulting in significant improvements in query accuracy, explanation clarity, and user satisfaction on standard benchmarks.
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.