Interesting and early. Worth a spike or exploration session.
Knowledge Graphs
Knowledge graphs plus LLMs (GraphRAG) deliver up to 99% search precision and traceable reasoning, but construction costs 3-5x more than baseline RAG and requires domain expertise most teams underestimate.
RAG·Infrastructure
neo4j.comOur Take
What It Is
Knowledge graphs represent information as interconnected entities and relationships — people, companies, concepts, events — stored in a graph database. GraphRAG combines this with LLMs: the graph provides structured context and relationship paths that ground the model's responses in verified, traceable data. Microsoft's GraphRAG (20K+ GitHub stars) and Neo4j's AI-native features (ai.* namespace, vector search with filters) are the primary production tools.
Why It Matters
Knowledge graphs are Emerging because the GraphRAG pattern is proving out in production but the construction barrier is still high. The value proposition is clear: up to 99% search precision on relationship-heavy queries, traceable provenance paths for every answer, and multi-hop reasoning that vector search cannot do. LinkedIn reduced ticket resolution time from 40 to 15 hours using a knowledge graph implementation.
The challenge is that building a useful knowledge graph from unstructured data requires domain expertise for schema design, LLM-based entity extraction (expensive), and ongoing maintenance. Most teams underestimate this effort.
Key Developments
- Feb 2026: Neo4j 2026.02.2 released (latest version).
- Jan 2026: Neo4j 2026.01 introduces vector search with metadata filters inside graph indexes.
- Dec 2025: Neo4j adds ai.* namespace for AI-native query language.
- 2025: Microsoft GraphRAG reaches 20K+ GitHub stars, integrated into Microsoft Discovery.
- 2025: Neo4j launches Infinigraph for 100TB+ distributed graph workloads.
What to Watch
Automated graph construction is the unlock. If LLM-based entity and relationship extraction becomes reliable enough to build production-quality graphs without extensive domain tuning, this moves to Promising quickly. Watch for Neo4j's LLM Knowledge Graph Builder maturation and for managed GraphRAG services that abstract the construction pipeline.
Strengths
- Up to 99% search precision: Knowledge graphs excel at multi-hop reasoning, entity disambiguation, and relationship traversal where vector search struggles.
- Reduces hallucination through grounding: Every answer has a verifiable provenance path traced through specific entities and relationships.
- Microsoft GraphRAG is open source: 20K+ stars with community detection, summarisation hierarchies, and local/global search out of the box.
- Neo4j AI-native evolution: ai.* namespace, vector search with filters, and LLM Knowledge Graph Builder lower the barrier to entry.
Considerations
- Construction cost is 3-5x baseline RAG: Entity extraction, relationship extraction, and domain-specific tuning add significant compute and human effort.
- Domain expertise required: Knowledge graphs need a thoughtful ontology. Poor schema design leads to poor retrieval regardless of model quality.
- Maintenance is ongoing: Knowledge graphs must be updated as source data changes. Stale graphs degrade faster than stale vector indexes.
- Scale requires infrastructure: Neo4j Infinigraph handles 100TB+ but requires enterprise licensing and significant infrastructure investment.
Resources
Articles
More in Data & Retrieval
Knowledge Graphs· Context Engineering· Data Mesh· Embedding Fine-tuning· GraphRAG· Synthetic Data· Contextual Retrieval· Document Parsing· Pinecone· Weaviate· LlamaIndex· pgvector
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