🧠 Knowledge Graph Generator

Transform unstructured text into interactive knowledge graphs using AI. Extract entities, relationships, and visualize complex information networks.

✨ Features

• 🔍 Entity Extraction: Identify people, organizations, locations, concepts
• 🔗 Relationship Detection: Discover connections between entities
• 📊 Interactive Visualization: D3.js-powered graph visualization
• 🗄️ Graph Database: Neo4j backend for complex queries
• 🎯 Smart Filtering: Filter by entity type, relationship strength
• 📈 Analytics: Network analysis, centrality measures
• 🌐 Web Interface: Explore graphs interactively
• 📤 Export: JSON, GraphML, CSV formats

🚀 Quick Start

# Install dependencies
pip install -r requirements.txt
python -m spacy download en_core_web_lg

# Start Neo4j (via Docker)
docker run -p 7474:7474 -p 7687:7687 neo4j:latest

# Run application
python src/main.py

# Process text
python src/cli.py --text "Your text here" --output graph.json

📖 Usage

from knowledge_graph import KnowledgeGraphGenerator

kg = KnowledgeGraphGenerator()

# Generate from text
text = "Apple Inc. was founded by Steve Jobs in Cupertino."
graph = kg.generate(text)

# Visualize
kg.visualize(graph, output="graph.html")

# Query
results = kg.query("Who founded Apple?")

🛠️ Technical Stack

• NLP: spaCy, NLTK
• LLM: GPT-4 for relationship extraction
• Graph DB: Neo4j
• Visualization: D3.js, Pyvis
• Backend: FastAPI
• Frontend: React + D3

📊 Example Output

{
  "entities": [
    {"id": "1", "name": "Apple Inc.", "type": "ORGANIZATION"},
    {"id": "2", "name": "Steve Jobs", "type": "PERSON"},
    {"id": "3", "name": "Cupertino", "type": "LOCATION"}
  ],
  "relationships": [
    {"from": "2", "to": "1", "type": "FOUNDED", "strength": 0.95},
    {"from": "1", "to": "3", "type": "LOCATED_IN", "strength": 0.88}
  ]
}

🎯 Use Cases

• Research: Analyze academic papers
• Business: Extract insights from reports
• Legal: Map case relationships
• Journalism: Investigate connections

📄 License

MIT License

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