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A minimal guide to using OWL ontologies to ground Cognee’s knowledge graphs. You’ll point Cognee at an ontology file during cognify and then ask ontology-aware questions. Before you start:
  • Complete Quickstart to understand basic operations
  • Read Ontologies to understand the concepts
  • Ensure you have LLM Providers configured
  • Have an OWL ontology file (.owl) in RDF/XML format
  • Have some text or files relevant to the ontology’s domain

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What Ontology Support Does

  • Grounds entities and relations to your OWL ontology (classes, individuals, properties)
  • Validates types via ontology domains/ranges and class hierarchy
  • Improves graph completion answers for domain-specific queries

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Step 1: Prepare an Ontology File

Start from a simple OWL file. Minimal ingredients:
  • Classes (e.g., TechnologyCompany, Car)
  • Individuals (e.g., Apple, Audi)
  • Object properties with domain/range (e.g., produces with domain=CarManufacturer, range=Car)
Example ontology files:
  • examples/python/ontology_input_example/basic_ontology.owl
  • examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl
Use any RDF/OWL editor (Protégé) to edit .owl files.
This example uses a simple ontology for demonstration. In practice, you can work with larger, more complex ontologies - the same approach works regardless of ontology size or complexity.

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Step 2: Add Your Data

Add either raw text or a directory. Keep it relevant to your ontology. 
import cognee

texts = [
    "Audi produces the R8 and e-tron.",
    "Apple develops iPhone and MacBook."
]

await cognee.add(texts)
# or: await cognee.add("/path/to/folder/of/files")
This simple example uses a list of strings for demonstration. In practice, you can add multiple documents, files, or entire datasets - the ontology processing works the same way across all your data.

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Step 3: Cognify Your Data + Ontologies

Create the config which contains the information about the ontology, to ground extracted entities/relations to the ontology. Then, simply pass the config to the cognify operation. 
import os
from cognee.modules.ontology.ontology_config import Config
from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver

ontology_path = os.path.join(
    os.path.dirname(os.path.abspath(__file__)), "ontology_input_example/basic_ontology.owl"
)

# Create full config structure manually
config: Config = {
    "ontology_config": {
        "ontology_resolver": RDFLibOntologyResolver(ontology_file=ontology_path)
    }
}

await cognee.cognify(config=config)
If omitted, Cognee builds a graph without ontology grounding. With an ontology, Cognee aligns nodes to classes/individuals and enforces property domain/range.

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Step 4: Ask Ontology-aware Questions

Use SearchType.GRAPH_COMPLETION to get answers that leverage ontology structure. 
from cognee.api.v1.search import SearchType

result = await cognee.search(
    query_type=SearchType.GRAPH_COMPLETION,
    query_text="What cars and their types are produced by Audi?",
)
print(result)
Phrase questions using ontology terms (class names, individual names, property language like “produces”, “develops”). If results feel generic, check that the ontology contains the expected classes/individuals and that your data mentions them.

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Code in Action

  • Small cars/tech demo: examples/python/ontology_demo_example.py
  • Medical comparison demo: examples/python/ontology_demo_example_2.py

Core Concepts

Understand ontology fundamentals

API Reference

Explore ontology API endpoints