用AI辅助专家协作生成领域知识问题，提升本体构建效率
IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineering
- 引入大模型与专家协同的迭代流程，自动提取并优化知识问题
- 在真实科研与文化遗产场景中验证，问题采纳率显著提升
- 全程可追溯，适合需要高质量知识建模的跨学科团队
能力问题（CQ）提取是本体工程中的关键但耗时环节，常因领域专家与本体工程师间沟通不畅而受阻。本文提出IDEA2，一种半自动化工作流，将大语言模型（LLM）融入专家参与的协作闭环中。流程包含：初始阶段由LLM从需求文档中提取CQ；专家在可访问的协作平台共同评审并反馈；对被拒的CQ，LLM基于反馈迭代重构直至达成共识。为确保透明与可复现，采用溯源模型记录每个CQ的完整编辑历史、匿名反馈及生成参数。在两个真实场景（科学数据、文化遗产）中验证表明，IDEA2能加速需求工程，提升生成问题的接受度与相关性，并在专家中展现出高可用性与有效性。代码与实验已公开于https://github.com/KE-UniLiv/IDEA2。
原文摘要 · Abstract (English)
Competency question (CQ) elicitation represents a critical but resource-intensive bottleneck in ontology engineering. This foundational phase is often hampered by the communication gap between domain experts, who possess the necessary knowledge, and ontology engineers, who formalise it. This paper introduces IDEA2, a novel, semi-automated workflow that integrates Large Language Models (LLMs) within a collaborative, expert-in-the-loop process to address this challenge. The methodology is characterised by a core iterative loop: an initial LLM-based extraction of CQs from requirement documents, a co-creational review and feedback phase by domain experts on an accessible collaborative platform, and an iterative, feedback-driven reformulation of rejected CQs by an LLM until consensus is achieved. To ensure transparency and reproducibility, the entire lifecycle of each CQ is tracked using a provenance model that captures the full lineage of edits, anonymised feedback, and generation parameters. The workflow was validated in 2 real-world scenarios (scientific data, cultural heritage), demonstrating that IDEA2 can accelerate the requirements engineering process, improve the acceptance and relevance of the resulting CQs, and exhibit high usability and effectiveness among domain experts. We release all code and experiments at https://github.com/KE-UniLiv/IDEA2
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