用大模型分步探索知识图谱，让普通人也能轻松查询数据。
ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
- 通过迭代探索与执行，逐步生成SPARQL查询
- 基于LLM的智能代理在文本转查询任务中表现更稳定
- 适合希望降低知识图谱使用门槛的研究者
非计算机背景用户难以直接操作知识图谱，因其查询语言SPARQL学习成本高。本文提出一种基于LLM的Text2SPARQL方法ARUQULA，受SPINACH启发，采用分步推理与交互式探索机制，而非一次性生成查询。该方法通过迭代式探索和执行路径，提升自然语言到SPARQL的转换准确性。研究详细阐述了系统架构与设计思路，并对代理行为进行深入分析，为后续优化提供依据。本工作源于Text2SPARQL挑战赛，旨在推动该领域技术进步。
原文摘要 · Abstract (English)
Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of exploration and execution. We describe the overall architecture and reasoning behind our design decisions, and also conduct a thorough analysis of the agent behavior to gain insights into future areas for targeted improvements. This work was motivated by the Text2SPARQL challenge, a challenge that was held to facilitate improvements in the Text2SPARQL domain.
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