cognee/docs/kr/guides/ontology-support.md

Ontology Quickstart

Step-by-step guide to using OWL ontologies to ground Cognee knowledge graphs

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

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

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.

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.

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.

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.

Code in Action

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

Understand ontology fundamentals Explore ontology API endpoints

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To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt