Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling

• URL: http://arxiv.org/abs/2604.02545v1
• Date: Thu, 02 Apr 2026 21:54:33 GMT
• Title: Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling
• Abstract summary: We propose a novel neuro-symbolic architecture grounded in Knowledge Graphs to generate narratives.<n>A key novelty of our approach is the repurposing of competency questions (CQs) into run-time executable narrative plans.<n>We validate this architecture using a new resource: the Live Aid KG, a multimodal dataset aligning 1985 concert data with the Music Meta Ontology.
• Score: 0.12891210250935145
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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