用知识图谱和能力问题构建可执行的叙事计划，确保文化遗产故事真实可查。
Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling
- 将能力问题转化为运行时可执行的叙事路径，实现精准知识检索
- 在Live Aid知识图谱上验证，符号化方法准确率最高，图遍历更连贯
- 适合需要高可信度的数字人文、文化遗产数字化项目
非物质文化遗产的保存面临集体记忆消逝的挑战。尽管大语言模型（LLMs）能生成吸引人的叙述，但其易产生事实错误或“幻觉”，在要求高真实性的情境下不可靠。为此，我们提出一种基于知识图谱（KG）的神经符号架构，建立透明的“规划-检索-生成”流程。核心创新是将传统设计阶段的能力问题（CQs）重用于运行时的可执行叙事计划，连接用户角色与原子级知识检索，确保生成过程有据可依且完全可审计。我们使用新构建的Live Aid KG——一个对齐1985年音乐会数据、音乐本体及外部多媒体资源的多模态数据集——进行验证。系统性比较三种检索增强生成（RAG）策略：纯符号的KG-RAG、文本增强的Hybrid-RAG、结构感知的Graph-RAG。实验揭示了事实精度、上下文丰富性与叙事连贯性间的可量化权衡。研究为个性化可控的叙事系统设计提供实用洞见。
原文摘要 · Abstract (English)
The preservation of intangible cultural heritage is a critical challenge as collective memory fades over time. While Large Language Models (LLMs) offer a promising avenue for generating engaging narratives, their propensity for factual inaccuracies or "hallucinations" makes them unreliable for heritage applications where veracity is a central requirement. To address this, we propose a novel neuro-symbolic architecture grounded in Knowledge Graphs (KGs) that establishes a transparent "plan-retrieve-generate" workflow for story generation. A key novelty of our approach is the repurposing of competency questions (CQs) - traditionally design-time validation artifacts - into run-time executable narrative plans. This approach bridges the gap between high-level user personas and atomic knowledge retrieval, ensuring that generation is evidence-closed and fully auditable. We validate this architecture using a new resource: the Live Aid KG, a multimodal dataset aligning 1985 concert data with the Music Meta Ontology and linking to external multimedia assets. We present a systematic comparative evaluation of three distinct Retrieval-Augmented Generation (RAG) strategies over this graph: a purely symbolic KG-RAG, a text-enriched Hybrid-RAG, and a structure-aware Graph-RAG. Our experiments reveal a quantifiable trade-off between the factual precision of symbolic retrieval, the contextual richness of hybrid methods, and the narrative coherence of graph-based traversal. Our findings offer actionable insights for designing personalised and controllable storytelling systems.
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