用自然语言解释帮助非专家逐步优化SPARQL查询
InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations
- 结合规则系统与大模型生成查询的自然语言解释
- 在标准测试中显著提升查询准确率和用户满意度
- 适合不懂编程但需查询语义网数据的用户
近年来，使用SPARQL查询语义网数据对非专家仍具挑战性，主要因其语法复杂且需理解数据结构。为此，我们提出InteracSPARQL，一个交互式SPARQL查询生成与优化系统，利用自然语言解释（NLEs）提升用户理解并支持迭代查询修正。该系统将大语言模型（LLMs）与基于规则的方法结合：先从SPARQL抽象语法树（AST）生成结构化解释，再通过大模型进行语言优化。用户可通过直接反馈或大模型驱动的自我修正来实时调整查询，纠正模糊或错误的部分。我们在标准基准上评估了InteracSPARQL，结果显示其在查询准确率、解释清晰度和整体用户满意度方面均优于基线方法。实验进一步表明，规则方法与大模型精修相结合，能构建更易用、更鲁棒的SPARQL交互界面。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。