GraphRAG is a Retrieval-Augmented Generation system that combines knowledge graphs with vector search to provide more accurate, context-aware AI responses. Unlike traditional RAG which only uses document embeddings, GraphRAG leverages the structured relationships and semantic connections in knowledge graphs to understand context and retrieve more relevant information.
In this comprehensive hands-on tutorial, I’ll demonstrate how to build a sophisticated GraphRAG system that combines the power of knowledge graphs with modern vector search capabilities. You’ll learn to implement:
Bidirectional Neo4j Integration — Flexible access to diverse graph data with seamless extraction and writing capabilities to Protege, enhancing your knowledge base and enabling it to evolve over time
Protégé Ontology Creation and Modification— Standardize and structure your data for improved semantic understanding and query precision, create Ontology from LLM directly or modify any existing ontology
Vector Database Storage — Optimize retrieval of relevant information, crucial for accurate RAG responses, Store retrieve Neo4J or Protege ontology to and from RAG store.
Semantic Search Capabilities — Deliver more meaningful and context-aware search results compared to traditional keyword-based approaches
NLP-Powered Querying — Simplify interactions through SPARQL and Cypher, making knowledge graphs accessible to users regardless of technical expertise
LLM-Driven Dynamic Ontology Creation — Enable quick adaptation to changing data needs and facilitate the creation of complex, evolving knowledge graphs
This robust GraphRAG implementation provides a complete workflow for managing and querying knowledge graphs. Whether you’re building fraud detection systems, recommendation engines, or intelligent search platforms, this tutorial will equip you with a flexible, comprehensive architecture that seamlessly integrates structured knowledge with semantic retrieval capabilities.
What you will need!
Protege https://github.com/protegeproject/protege
Neo4j AuraDB — https://console-preview.neo4j.io/
GraphRag Plugin — https://github.com/vishalmysore/graphrag
VidyaAstra Plugin — https://github.com/vishalmysore/vidyaastra-protege-plugin
Neo4j Protege plugin — https://github.com/vishalmysore/neo4j-protege-plugin
The ontology used in this example is here https://github.com/vishalmysore/graphrag/blob/main/fraud-detection-ontology.owl
Please note I am actively working on these plugins and will be refactoring some package names and adding more features, I will do my best to make sure they are always in working condition but if not please feel free to reach out to me.
Once all the 3 plugins are installed they should be available here
Connecting to Neo4J
You can connect to Neo4J AuraDB instance using the plugin , it should be available in
With this plugin you should be able to connect to the instance and import export ontology from neo4j to owl format or rdf format. You can get the ontology locally and apply all queries in straight forward NLP you can also import subset of graphs and use it in your local ontology
You can run query in plain English
Import to Protege
and then import them to ontology
Build RAG Store
Once you import to Protege you can then create embeddings in local rag
Now you can query you RAG like a normal RAG
Lets take the Fraud Detection use case
Ontology for the use case is here https://github.com/vishalmysore/graphrag/blob/main/fraud-detection-ontology.owl
Once you import that in your protege you can then store it in RAG using GraphRAG plugin
Combine it with Vidyaastra Plugin to Get more info and drill down into the subgraph
Use Features in Plugin such as Explain graph , NLP query and much more .
You can create , modify ontology and import locally as well
That feature is avaiable from the tools menu
This tutorial demonstrates a feature-rich GraphRAG implementation that opens new possibilities for students and researchers exploring knowledge graph technologies. By combining Protégé, Neo4j, LLMs, and Qdrant, I have created a comprehensive system that goes far beyond basic RAG implementations.
The platform provides essential capabilities including seamless import/export to Neo4j for flexible data management, natural language query translation to both Cypher and SPARQL (eliminating the need to learn complex query languages), and local vector storage with Qdrant that can easily extend to cloud-based solutions. The ability to focus on specific subgraphs enables targeted analysis, while LLM-powered ontology creation, modification, and exploration empowers users to dynamically shape their knowledge structures without deep ontology engineering expertise.
This integration of Protégé’s semantic modeling, Neo4j’s graph database capabilities, LLM intelligence, and Qdrant’s vector search represents a new paradigm in GraphRAG systems. It provides students and researchers with an accessible, powerful toolkit to explore the intersection of knowledge graphs and retrieval-augmented generation — transforming what was once a complex, expert-level endeavor into an intuitive, feature-rich platform for innovation and discovery.
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