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English(EN) Graph Query Generation with Constraint-guided Large Language Agents

LLM代理通过约束引导的Chase & Backchase生成图查询

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了UniQGen，一个使用大型语言模型代理生成图查询的新框架。该方法扩展了Chase & Backchase算法，以动态提取和优化查询子句，支持Cypher等多种查询语言，超越了典型的RDF/SPARQL。在GraphQ和GrailQA等基准测试上的评估显示，与现有方法相比，准确性和效率有了显著提高。 AI

影响 通过支持多种查询语言和提高准确性，增强了企业级知识图谱问答能力。

排序理由 这是一篇详细介绍图查询生成新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM代理通过约束引导的Chase & Backchase生成图查询

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这是一篇详细介绍图查询生成新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mengying Wang, Nicolaas Jedema, Rahul Pandey, RaviKiran Krishnan, Jens Lehmann, Yinghui Wu ·

    Constraint-guided Large Language Agents for Graph Query Generation

    arXiv:2605.00845v1 Announce Type: cross Abstract: Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and property graphs underexplored, despite increasing demand for unified KGQA in indust…

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