Skip to main content

​
Ontology Editor

The Graphora Ontology Editor is a powerful tool for defining the structure, validation rules, and quality constraints of your knowledge graphs. It provides a comprehensive YAML-based format for describing entities, relationships, properties, and sophisticated quality validation rules.

​
Key Features

YAML-Based Schema

Define your ontology using intuitive YAML syntax with full schema validation

Quality Rules

Built-in quality validation with format rules, business rules, and automated scoring

Version Control

Track ontology changes with full version history and rollback capabilities

Real-time Validation

Instant feedback on syntax errors and schema violations during editing

​
What You Can Define

​
1. Entities & Properties

Define the core building blocks of your knowledge graph with typed properties, constraints, and descriptions. 
entities:
  Company:
    properties:
      name:
        type: string
        description: "Company legal name"
        required: true
        unique: true
      revenue:
        type: integer
        description: "Annual revenue in USD"

​
2. Relationships

Specify how entities connect to each other with cardinality constraints and relationship properties. 
relationships:
  ACQUIRED_BY:
    target: Company
    cardinality: manyToOne
    properties:
      acquisitionDate:
        type: string
        description: "Date of acquisition (YYYY-MM-DD)"

​
3. Quality Rules

Enforce data quality with comprehensive validation rules including format constraints, business rules, and automated quality scoring. 
quality:
  format:
    pattern: "^[A-Z][a-zA-Z0-9\\s&.,-]+$"
    minLength: 2
    maxLength: 100
  business:
    forbiddenValues: ["Unknown", "N/A", "TBD"]

​
Ontology Management Workflow

1

Design

Plan your ontology structure, identifying key entities, relationships, and business rules
2

Create

Use the YAML editor to define entities, properties, relationships, and quality rules
3

Validate

Leverage real-time validation to ensure syntax correctness and schema compliance
4

Test

Validate your ontology against sample data to ensure it meets your requirements
5

Deploy

Register your ontology and use it for knowledge graph extraction and transformation

​
Getting Started

The ontology editor supports both simple ontology definitions and complex quality-aware schemas:

​
Basic Ontology

Perfect for getting started with simple entity definitions: 
version: 1
entities:
  Person:
    properties:
      name:
        type: string
        required: true
      age:
        type: integer
    relationships:
      WORKS_FOR:
        target: Company

​
Quality-Aware Ontology

Advanced schemas with comprehensive validation rules: 
version: 1
entities:
  Company:
    properties:
      name:
        type: string
        required: true
        quality:
          format:
            pattern: "^[A-Z][a-zA-Z0-9\\s&.,-]+$"
            minLength: 2
            maxLength: 100
          business:
            forbiddenValues: ["Unknown Company", "N/A", "TBD"]

​
Quality Validation System

The ontology editor includes a sophisticated quality validation system that:
  • Scores extracted data on a 0-100 scale with letter grades (A-F)
  • Validates format rules like patterns, length, and case formatting
  • Enforces business rules including allowed/forbidden values and ranges
  • Provides detailed violation reports with context and suggestions
  • Enables auto-approval for high-quality extractions

​
API Integration

Ontologies created in the editor integrate seamlessly with the Graphora API: 
from graphora import GraphoraClient

client = GraphoraClient(user_id="your-user-id")

# Register your ontology
ontology_response = client.register_ontology(ontology_yaml)

# Use it for transformation
transform_response = client.transform_document(
    file_path="document.pdf",
    ontology_id=ontology_response.id
)

​
Next Steps

Learn the YAML Syntax

Master the complete YAML syntax for defining entities, properties, and relationships

Explore Quality Rules

Implement sophisticated quality validation and automated scoring

See Examples

Browse complete ontology examples for different domains and use cases