CQs as Executable Plans for Controlled RAG

🔑 Enhanced Key Takeaways

• •The approach addresses the 'hallucination-by-omission' problem in RAG by enforcing a strict evidence-closed constraint, where the LLM is restricted to generating content only from the subgraph retrieved via the CQ-derived plan.
• •The Live Aid KG utilizes the Music Meta Ontology (MMO) to bridge structured concert metadata with unstructured multimedia assets, enabling cross-modal retrieval that standard vector-based RAG often fails to capture.
• •The study identifies that while Graph-RAG excels in structural connectivity, it often suffers from 'context dilution' in long-form narrative tasks, a limitation this CQ-based planning architecture specifically mitigates by prioritizing path-based relevance over global graph density.

🛠️ Technical Deep Dive

• •Architecture: Neuro-symbolic pipeline where a 'Planner' LLM decomposes user queries into a sequence of SPARQL-like executable operations based on predefined Competency Questions (CQs).
• •Execution Engine: A runtime interpreter that maps CQ-derived plans to specific graph traversal patterns, ensuring the retrieved evidence is strictly aligned with the narrative requirements.
• •Dataset Composition: The Live Aid KG integrates 1985 concert performance logs, artist discographies, and event-specific multimedia metadata, structured to support multi-hop reasoning across temporal and spatial dimensions.
• •Evaluation Metrics: Uses a combination of ROUGE-L for coherence, FactScore for factual consistency, and a custom 'Plan-Adherence' metric to measure how strictly the generation follows the CQ-derived execution path.

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline