用模板+大模型生成知识图谱问答，既快又准。
KGQuest: Template-Driven QA Generation from Knowledge Graphs with LLM-Based Refinement
- 按关系聚类三元组，生成可复用的自然语言模板。
- 大模型优化模板，提升语义流畅性且不改事实。
- 从知识图谱选干扰项，生成高质量选择题。
从知识图谱（KG）生成问答对在教育平台、知识传播和大语言模型测试中至关重要，但现有方法常面临可扩展性差、语言质量低和事实不一致的问题。本文提出一种可扩展且确定性的问答生成流水线：首先基于关系对KG三元组进行聚类，通过实体类型与关系推导出自然语言规则，生成可复用模板；随后利用大语言模型（LLM）对模板进行精炼，提升表达清晰度与连贯性，同时保证事实准确；最后采用选择策略从知识图谱中引入干扰项，完成答案选项的实例化。实验表明，该混合方法能高效生成高质量问答对，在保持高可扩展性的同时兼具语言流畅性与精准性。
原文摘要 · Abstract (English)
The generation of questions and answers (QA) from knowledge graphs (KG) plays a crucial role in the development and testing of educational platforms, dissemination tools, and large language models (LLM). However, existing approaches often struggle with scalability, linguistic quality, and factual consistency. This paper presents a scalable and deterministic pipeline for generating natural language QA from KGs, with an additional refinement step using LLMs to further enhance linguistic quality. The approach first clusters KG triplets based on their relations, creating reusable templates through natural language rules derived from the entity types of objects and relations. A module then leverages LLMs to refine these templates, improving clarity and coherence while preserving factual accuracy. Finally, the instantiation of answer options is achieved through a selection strategy that introduces distractors from the KG. Our experiments demonstrate that this hybrid approach efficiently generates high-quality QA pairs, combining scalability with fluency and linguistic precision.
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