Adaptive GraphRAG: A Framework for Knowledge Graph Quality, Consistency, and Evolution

Session Track: Knowledge Graphs & GraphRAG

Session Time: 09:00 am - 09:30 am April 15

Session description

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.