Repurposing competency questions as runtime executable plans creates a controlled neuro-symbolic RAG architecture that produces evidence-closed stories from knowledge graphs.
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LLM-generated competency questions exhibit distinct profiles in readability, relevance, and complexity that vary by model type and use case.
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Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling
Repurposing competency questions as runtime executable plans creates a controlled neuro-symbolic RAG architecture that produces evidence-closed stories from knowledge graphs.
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Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models
LLM-generated competency questions exhibit distinct profiles in readability, relevance, and complexity that vary by model type and use case.