Neo4j GraphRAG: Intelligent Knowledge Graph Querying with AI

Source: Dev.to

What is GraphRAG?

GraphRAG (Graph‑based Retrieval‑Augmented Generation) is an advanced AI technique that combines:

• Knowledge graph traversal and graph neural networks
• Semantic vector search over structured graph data
• Large Language Models (LLMs) for natural language understanding
• Ontology reasoning for intelligent knowledge retrieval

Code for this article is here:

GraphRAG vs Traditional RAG

flowchart LR
    subgraph Traditional_RAG
        D[Documents] --> C[Text chunks] --> V[Vector embeddings] --> S[Similarity search] --> L[LLM]
    end
    subgraph GraphRAG
        KG[Knowledge Graph + Ontology] --> ER[Entity/Relationship extraction] --> SE[Structure‑aware embeddings] --> GR[Graph‑based retrieval] --> CL[Context‑enriched LLM]
    end

What Does This Project Do?

This project is a Protégé GraphRAG plugin that combines graph databases, ontology engineering, and AI‑powered semantic search to enable intelligent querying of knowledge graphs using natural language.

Core Functionality

┌─────────────────────────────────────────────────────────────────┐
│                    User Experience                              │
├─────────────────────────────────────────────────────────────────┤
│  User asks: "What are all the classes in my ontology and       │
│              how are they related?"                            │
│                                                                 │
│  Plugin:                                                        │
│    1. Searches the indexed ontology (semantic search)          │
│    2. Retrieves relevant classes and relationships             │
│    3. Sends context + question to AI (GPT‑4, Claude, etc.)    │
│    4. Returns natural language answer                          │
│                                                                 │
│  Answer: "Your ontology contains the following main classes:   │
│           - Person (subclass of Agent)                         │
│           - Organization (subclass of Agent)                   │
│           They are related through the 'worksFor' property..." │
└─────────────────────────────────────────────────────────────────┘

Key Features of This GraphRAG System

• Graph‑Based Natural Language Querying – Ask questions about your knowledge graph and ontology in plain English instead of writing SPARQL or Cypher queries.
• Dual Knowledge Graph Sources
  • Query your OWL ontology (RDF triples, RDFS/OWL axioms, class hierarchies, property chains).
  • Query your Neo4j property graph (labeled nodes, typed relationships, graph properties).
• GraphRAG‑Powered Answers – Retrieval‑Augmented Generation leverages:
  • Graph topology (node neighborhoods, relationship paths)
  • Semantic embeddings (vector similarity search)
  • Ontology reasoning (inference, subsumption hierarchies)
  • Your actual graph data, not AI hallucinations.
• In‑Memory Vector Store – Fast semantic search and embedding‑based retrieval without external vector‑database dependencies.
• Seamless Protégé Integration – Works directly inside Protégé’s UI with full OSGi plugin architecture.

Why Combine Neo4j + GraphRAG + Protégé?

The Problem Each Technology Solves

Advantages of This Combination

Protégé: The Ontology Foundation

What Protégé Provides

• Industry‑standard tool for creating and editing OWL ontologies.
• Built‑in reasoners (HermiT, Pellet) for logical inference.
• Visualization of class hierarchies and relationships.
• Plugin architecture for extensibility.

Limitations Without This Plugin

• ❌ Querying requires SPARQL knowledge.
• ❌ No integration with external graph databases.
• ❌ No natural language interface.
• ❌ Limited to OWL reasoning only.

With This Plugin

• ✅ Ask questions in plain English.
• ✅ Combine OWL ontology + Neo4j graph data.
• ✅ AI‑powered insights from your ontology.
• ✅ No need to learn SPARQL.

Neo4j: The Graph Database Power

What Neo4j Provides

• Property graph model – richer than RDF triples.
• Cypher query language – more intuitive than SPARQL.
• Built‑in graph algorithms (PageRank, shortest path, community detection).
• Scalability to billions of nodes and relationships.

Example Use Case

// Find influential people in an organization
MATCH (p:Person)-[:WORKS_FOR]->(o:Organization)
WHERE o.name = 'TechCorp'
RETURN p.name, size((p)-[:MANAGES]->()) AS team_size
ORDER BY team_size DESC

Limitations Without RAG

• ❌ Requires learning Cypher syntax.
• ❌ Hard to explore data without knowing structure.
• ❌ Complex queries are intimidating for non‑technical users.

With RAG + Neo4j

• ✅ Ask: “Who manages the largest teams at TechCorp?”
• ✅ Plugin translates natural language → retrieves context → generates answer.
• ✅ Non‑technical users can query the graph.

GraphRAG: The Knowledge Graph Intelligence Layer

What GraphRAG Provides

1. Graph‑Aware Retrieval – Semantic search + graph traversal to find relevant subgraphs.

   Query: "What is a Person?"
     ↓
   Graph Traversal + Vector Search finds:
   - Structure: Person --subClassOf--> Agent
   - Properties: Person --hasProperty--> name, age
   - Instances: John --rdf:type--> Person
   - Related Paths: Person --worksFor--> Organization

2. Context Enrichment – Expand retrieved nodes with graph neighborhoods.

   Initial Results → Graph Expansion → Related Entities
   Person node → Traverse 2‑hops →
     - Superclasses (Agent, Thing)
     - Properties (name, age, email)
     - Instances (John, Jane)
     - Relationships (worksFor, manages)

3. LLM Generation – Create natural‑language answers using graph‑enriched context.

   Graph Context + Question → LLM → Answer
   "A Person is a subclass of Agent in your ontology.
    Persons have properties like name and age.
    In your knowledge graph, John is an instance of Person.
    Persons can work for Organizations via the 'worksFor' relationship."

Why GraphRAG Is Superior to Pure LLM and Traditional RAG

• Accuracy – Answers are grounded in actual graph data and ontology reasoning.
• Context – Retrieval includes both semantic similarity and explicit graph structure.
• Reduced Hallucinations – The LLM only generates text based on verified graph context.