A Graph RAG (Retrieval-Augmented Generation) chat application that combines OpenAI GPT with knowledge graphs stored in GraphDB
After seeing yet another Graph RAG demo using Neo4j with no ontology, I decided to show what real semantic Graph RAG looks like.
The Problem with Most Graph RAG Demos:
Everyone's building Graph RAG with LPG databases (Neo4j, TigerGraph, Arrango etc.) and calling it "knowledge graphs." But here's the thing:
Without formal ontologies, you don't have a knowledge graph—you just have a graph database.
The difference?
❌ LPG: Nodes and edges are just strings. No semantics. No reasoning. No standards.
✅ RDF/SPARQL: Formal ontologies (RDFS/OWL) that define domain knowledge. Machine-readable semantics. W3C standards. Built-in reasoning.
So I Built a Real Semantic Graph RAG
Using:
- Microsoft Agent Framework - AI orchestration
- Formal ontologies - RDFS/OWL knowledge representation
- Ontotext GraphDB - RDF triple store
- SPARQL - semantic querying
- GPT-5 - ontology-aware extraction
It's all on github, a simple template as boilerplate for you project:
The "Jaguar problem":
What does "Yesterday I was hit by a Jaguar" really mean? It is impossible to know without concept awareness. To demonstrate why ontologies matter, I created a corpus with mixed content:
🐆 Wildlife jaguars (Panthera onca)
🚗 Jaguar cars (E-Type, XK-E)
🎸 Fender Jaguar guitars
I fed this to GPT-5 along with a jaguar conservation ontology.
The result? The LLM automatically extracted ONLY wildlife-related entities—filtering out cars and guitars—because it understood the semantic domain from the ontology.
No post-processing. No manual cleanup. Just intelligent, concept-aware extraction.
This is impossible with LPG databases because they lack formal semantic structure. Labels like (:Jaguar) are just strings—the LLM has no way to know if you mean the animal, car, or guitar.
Knowledge Graphs = "Data for AI"
LLMs don't need more data—they need structured, semantic data they can reason over.
That's what formal ontologies provide:
✅ Domain context
✅ Class hierarchies
✅ Property definitions
✅ Relationship semantics
✅ Reasoning rules
This transforms Graph RAG from keyword matching into true semantic retrieval.
Check Out the Full Implementation, the repo includes:
Complete Graph RAG implementation with Microsoft Agent Framework
Working jaguar conservation knowledge graph
Jupyter notebook: ontology-aware extraction from mixed-content text
https://lnkd.in/dmf5HDRm
And if you have gotten this far, you realize that most of this post is written by Cursor ... That goes for the code too. 😁
Your Turn:
I know this is a contentious topic. Many teams are heavily invested in LPG-based Graph RAG. What are your thoughts on RDF vs. LPG for Graph RAG? Drop a comment below!
#GraphRAG #KnowledgeGraphs #SemanticWeb #RDF #SPARQL #AI #MachineLearning #LLM #Ontology #KnowledgeRepresentation #OpenSource #neo4j #graphdb #agentic-framework #ontotext #agenticai | 148 comments on LinkedIn
