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Use Ontologies

Difficulty: Easy

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Overview

In this tutorial, you’ll learn how to build a knowledge graph using the Cognee framework together with your own custom ontology. We’ll guide you through adding text data, integrating an OWL-based ontology to enrich your graph, and querying for insights. By the end, you will have:
  • Added raw text data representing information about car manufacturers and technology companies
  • Integrated a custom ontology (OWL file) that defines semantic relationships
  • Run the Cognee pipeline to generate and inspect your knowledge graph
  • Queried the graph to extract specific insights

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What You’ll Learn

  • Environment Setup: Installing Cognee and preparing your workspace
  • Data Ingestion: Adding raw text data for processing
  • Ontology Integration: Using a custom ontology to structure your data
  • Graph Creation: Running the pipeline to build your knowledge graph
  • Query & Visualization: Searching the graph and visualizing the results

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Prerequisites

Before starting this tutorial, ensure you have:
  • Python 3.9 to 3.12 installed
  • Basic understanding of knowledge graphs and ontologies
  • Familiarity with OWL (Web Ontology Language) concepts
  • Text editor for creating script files

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Step 1: Install Cognee

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Set Up Development Environment

Navigate to the Cognee repository and install with all dependencies: 
cd cognee
uv sync --dev --all-extras --reinstall
This installs Cognee with all necessary dependencies for ontology processing and graph generation.

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Step 2: Prepare Your Text Data

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Create Sample Data

Create a Python script and define your text data. For this tutorial, we’ll use information about car manufacturers and technology companies: 
text_1 = '''
1. Audi
Audi is known for its modern designs and advanced technology. Founded in the early 1900s, the brand has earned a reputation for precision engineering and innovation. With features like the Quattro all-wheel-drive system, Audi offers a range of vehicles from stylish sedans to high-performance sports cars.

2. BMW
BMW, short for Bayerische Motoren Werke, is celebrated for its focus on performance and driving pleasure. The company's vehicles are designed to provide a dynamic and engaging driving experience, and their slogan, "The Ultimate Driving Machine," reflects that commitment. BMW produces a variety of cars that combine luxury with sporty performance.

3. Mercedes-Benz
Mercedes-Benz is synonymous with luxury and quality. With a history dating back to the early 20th century, the brand is known for its elegant designs, innovative safety features, and high-quality engineering. Mercedes-Benz manufactures not only luxury sedans but also SUVs, sports cars, and commercial vehicles, catering to a wide range of needs.

4. Porsche
Porsche is a name that stands for high-performance sports cars. Founded in 1931, the brand has become famous for models like the iconic Porsche 911. Porsche cars are celebrated for their speed, precision, and distinctive design, appealing to car enthusiasts who value both performance and style.

5. Volkswagen
Volkswagen, which means "people's car" in German, was established with the idea of making affordable and reliable vehicles accessible to everyone. Over the years, Volkswagen has produced several iconic models, such as the Beetle and the Golf. Today, it remains one of the largest car manufacturers in the world, offering a wide range of vehicles that balance practicality with quality.
'''

text_2 = '''
1. Apple
Apple is renowned for its innovative consumer electronics and software. Its product lineup includes the iPhone, iPad, Mac computers, and wearables like the Apple Watch. Known for its emphasis on sleek design and user-friendly interfaces, Apple has built a loyal customer base and created a seamless ecosystem that integrates hardware, software, and services.

2. Google
Founded in 1998, Google started as a search engine and quickly became the go-to resource for finding information online. Over the years, the company has diversified its offerings to include digital advertising, cloud computing, mobile operating systems (Android), and various web services like Gmail and Google Maps. Google's innovations have played a major role in shaping the internet landscape.

3. Microsoft
Microsoft Corporation has been a dominant force in software for decades. Its Windows operating system and Microsoft Office suite are staples in both business and personal computing. In recent years, Microsoft has expanded into cloud computing with Azure, gaming with the Xbox platform, and even hardware through products like the Surface line. This evolution has helped the company maintain its relevance in a rapidly changing tech world.

4. Amazon
What began as an online bookstore has grown into one of the largest e-commerce platforms globally. Amazon is known for its vast online marketplace, but its influence extends far beyond retail. With Amazon Web Services (AWS), the company has become a leader in cloud computing, offering robust solutions that power websites, applications, and businesses around the world. Amazon's constant drive for innovation continues to reshape both retail and technology sectors.

5. Meta
Meta, originally known as Facebook, revolutionized social media by connecting billions of people worldwide. Beyond its core social networking service, Meta is investing in the next generation of digital experiences through virtual and augmented reality technologies, with projects like Oculus. The company's efforts signal a commitment to evolving digital interaction and building the metaverse—a shared virtual space where users can connect and collaborate.
'''
This sample data provides rich information about companies that will be structured using our custom ontology.

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Step 3: Create Your Custom Ontology

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Define Ontology Structure

Create a custom ontology file in OWL format. You can find a complete example at: Ontology file example Here’s a sample structure for basic_ontology.owl: 
<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
   xmlns:ns1="http://example.org/ontology#"
   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
   xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
>
  <rdf:Description rdf:about="http://example.org/ontology#Volkswagen">
    <rdfs:comment>Created for making cars accessible to everyone.</rdfs:comment>
    <ns1:produces rdf:resource="http://example.org/ontology#VW_Golf"/>
    <ns1:produces rdf:resource="http://example.org/ontology#VW_ID4"/>
    <ns1:produces rdf:resource="http://example.org/ontology#VW_Touareg"/>
    <rdf:type rdf:resource="http://example.org/ontology#CarManufacturer"/>
    <rdf:type rdf:resource="http://www.w3.org/2002/07/owl#NamedIndividual"/>
  </rdf:Description>
  <!-- Additional ontology definitions follow -->
</rdf:RDF>
This ontology defines relationships between car manufacturers and their products, providing semantic structure to our knowledge graph.

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Step 4: Build the Complete Pipeline

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Create Main Script

Create your main Python script with the complete pipeline. You can find a working demo at: Ontology demo example 
import cognee
import asyncio
import logging
import os

from cognee.api.v1.search import SearchType
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.shared.utils import setup_logging

# Define your text data (insert your full texts for text_1 and text_2)
text_1 = """insert full text_1 here"""
text_2 = """insert full text_2 here"""

async def main():
    # Step 1: Reset data and system state
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    # Step 2: Add text data
    text_list = [text_1, text_2]
    await cognee.add(text_list)

    # Step 3: Create knowledge graph using the custom ontology
    ontology_path = os.path.join(
        os.path.dirname(os.path.abspath(__name__)), "ontology_input_example/basic_ontology.owl"
    )
    pipeline_run = await cognee.cognify(ontology_file_path=ontology_path)
    print("Knowledge with ontology created.")

    # Step 4: Calculate descriptive metrics
    await cognee.get_pipeline_run_metrics(pipeline_run, include_optional=True)
    print("Descriptive graph metrics saved to database.")

    # Step 5: Query insights from the graph
    search_results = await cognee.search(
        query_type=SearchType.GRAPH_COMPLETION,
        query_text="What are the exact cars and their types produced by Audi?",
    )
    print(search_results)

    # Step 6: Visualize the knowledge graph and save it to HTML
    await visualize_graph()

if __name__ == "__main__":
    setup_logging(logging.INFO)
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        loop.run_until_complete(main())
    finally:
        loop.run_until_complete(loop.shutdown_asyncgens())
This script demonstrates the complete workflow: data ingestion, ontology integration, graph creation, visualization, and querying.

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Script Functionality

This script will:
  • Reset any previous data for a clean start
  • Add your text data to the system
  • Use your custom ontology to create a semantically enriched graph
  • Calculate graph metrics for analysis
  • Query the graph for specific insights
  • Visualize the resulting knowledge graph from the generated graph_visualization.html file.

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Step 5: Execute Your Pipeline

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Run the Script

Execute your script from the terminal: 
python your_script.py
The script will process your data through the ontology-enhanced pipeline and generate visualizations and insights.

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Expected Output

You should see output similar to: 
Knowledge with ontology created.
Descriptive graph metrics saved to database.
[Search results about Audi cars and their types]
Below, you’ll find an example graph visualization:

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Summary

In this tutorial, you learned how to:
  • Set Up: Install Cognee and prepare your environment
  • Add Data: Ingest raw text data about car manufacturers and technology companies
  • Integrate an Ontology: Use a custom OWL ontology to define relationships
  • Build the Graph: Run the pipeline to generate a knowledge graph
  • Query & Visualize: Retrieve specific insights and visualize the graph
This method not only helps you organize complex data but also leverages semantic relationships to enhance your data insights through structured ontological knowledge.

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Next Steps

Now that you’ve mastered ontology integration, you can:
  • Create more complex ontologies with additional relationship types
  • Integrate multiple ontologies for richer semantic structures
  • Build domain-specific knowledge graphs for specialized use cases
  • Explore advanced querying techniques with your ontology-enhanced graphs