Abstract
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.
Abstract (translated)
非物质文化遗产的保护是一项关键挑战，因为集体记忆会随时间逐渐消逝。尽管大语言模型（LLMs）为生成引人入胜的叙事提供了前景可期的途径，但其产生事实错误或“幻觉”的倾向使其在真实性为核心要求的遗产应用中并不可靠。为应对这一问题，我们提出了一种基于知识图谱（KGs）的新型神经符号架构，该架构建立了透明的“规划-检索-生成”叙事生成工作流。本方法的关键创新在于将能力问题（CQs）——传统上作为设计阶段验证工件——重新利用为运行时可执行的叙事规划。该方法弥合了高层用户画像与原子级知识检索之间的鸿沟，确保生成过程具备证据封闭性且完全可审计。 我们利用一项新资源——Live Aid知识图谱对该架构进行验证：这是一个多模态数据集，将1985年演唱会数据与音乐元本体对齐，并链接至外部多媒体资源。我们在此图谱上系统比较了三种不同的检索增强生成（RAG）策略：纯符号化的知识图谱检索增强生成、文本增强的混合检索增强生成，以及结构感知的图检索增强生成。实验揭示了符号检索的事实精确性、混合方法的语境丰富性与基于图谱遍历的叙事连贯性之间存在可量化的权衡。我们的研究结果为设计个性化且可控的叙事系统提供了可操作的见解。
URL
https://arxiv.org/abs/2604.02545
