InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations

TL;DR

InteracSPARQL is an interactive system that uses natural language explanations and large language models to help users generate and refine SPARQL queries more easily, especially for non-experts.

Contribution

It introduces a novel hybrid approach combining rule-based explanations with LLM-based refinements for improved SPARQL query interaction.

Findings

Significant improvement in query accuracy over baselines

Enhanced explanation clarity and user satisfaction

Effective combination of rule-based and LLM methods

Abstract

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.