用大模型让普通人也能轻松查询知识图谱。
Augmented Knowledge Graph Querying leveraging LLMs
- 通过RAG框架结合模板增强大模型生成SPARQL查询。
- 在多个数据集上准确率超90%，显著降低语义错误。
- 适合工业场景中非专家用户快速获取结构化数据。
将知识图谱（KG）作为结构化、语义导向的数据表示模型，显著提升了不同领域中的数据集成、推理与查询能力，尤其在工业5.0背景下，人、智能设备与生产流程产生的数据融合至关重要。然而，使用正式查询语言管理、检索和可视化知识图谱对非专家用户而言技术门槛高，限制了其在工业环境中的应用。为此，我们提出SparqLLM框架，采用检索增强生成（RAG）方案，提升知识图谱查询能力。该框架执行提取、转换与加载（ETL）流程，从原始数据构建知识图谱，并配备由大语言模型（LLM）驱动的自然语言接口，实现自动SPARQL查询生成。通过将基于模板的方法作为检索上下文输入给LLM，SparqLLM增强了查询可靠性，减少语义错误，确保更精准高效的交互。此外，系统还集成动态可视化仪表板，根据返回数据结构自适应呈现结果，提升可读性。严格的实验评估表明，SparqLLM在查询准确率、鲁棒性及用户体验方面表现优异，是访问语义数据的可扩展解决方案。
原文摘要 · Abstract (English)
Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities across different domains. This is especially true in modern scenarios such as Industry 5.0, in which the integration of data produced by humans, smart devices, and production processes plays a crucial role. However, the management, retrieval, and visualization of data from a KG using formal query languages can be difficult for non-expert users due to their technical complexity, thus limiting their usage inside industrial environments. For this reason, we introduce SparqLLM, a framework that utilizes a Retrieval-Augmented Generation (RAG) solution, to enhance the querying of Knowledge Graphs (KGs). SparqLLM executes the Extract, Transform, and Load (ETL) pipeline to construct KGs from raw data. It also features a natural language interface powered by Large Language Models (LLMs) to enable automatic SPARQL query generation. By integrating template-based methods as retrieved-context for the LLM, SparqLLM enhances query reliability and reduces semantic errors, ensuring more accurate and efficient KG interactions. Moreover, to improve usability, the system incorporates a dynamic visualization dashboard that adapts to the structure of the retrieved data, presenting the query results in an intuitive format. Rigorous experimental evaluations demonstrate that SparqLLM achieves high query accuracy, improved robustness, and user-friendly interaction with KGs, establishing it as a scalable solution to access semantic data.
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。