NODES AI 2026 – Adaptive GraphRAG: A Framework for Knowledge Graph Quality, Consistency, Evolution

Join Joshua Yu at NODES AI for this session: "Adaptive GraphRAG: A Framework for Knowledge Graph Quality, Consistency, and Evolution".

Most GraphRAG systems focus heavily on extracting triples from text, but very few address the real long-term challenge: How do we keep a knowledge graph accurate, consistent, and trustworthy as new information arrives? In real-world deployments, KGs decay — entities drift, duplicates accumulate, contradictions appear, and retrieval quality drops. This talk introduces Adaptive GraphRAG, a practical Neo4j-based framework for sustaining KG quality through continuous enrichment and evolution. Using Cypher, GDS, and LLM-powered analysis, we show how to:

• Detect and repair coreference, entity drift, and semantic contradictions
• Enforce entity consistency and canonical forms across ingestion cycles
• Use GDS similarity, clustering, and connectivity analysis for deduplication and quality scoring
• Measure KG health with structural metrics like giant-component growth
• Build iterative evolution loops that keep GraphRAG accurate as the corpus expands

If you're deploying GraphRAG or enterprise knowledge systems, this talk offers practical mechanisms to maintain high-quality, self-improving knowledge graphs over time.