Transform 20 million Power BI dashboards into AI-ready ontologies
Installation • Quick Start • Documentation • Examples • Contributing
As detailed in my Medium article "The Power BI Ontology Paradox", enterprises have 20+ million Power BI semantic models that are actually informal ontologies trapped in proprietary .pbix files.
- The Challenge: Each Power BI model contains entities, relationships, and business logic—but AI agents can't access this semantic intelligence
- The Cost: Enterprises spend $50K-$200K per semantic definition to reconcile conflicts across dashboards
- The Impact: This creates billions in "semantic debt" and prevents AI agents from functioning at scale
- The $4.6M Mistake: A logistics company lost $4.6M when an AI agent used a renamed column (
Warehouse_Location→FacilityID) because there was no semantic binding validation
PowerBI Ontology Extractor unlocks the hidden ontologies in your Power BI dashboards and transforms them into formal, AI-ready ontologies.
# In 3 lines of code:
extractor = PowerBIExtractor("Supply_Chain_Operations.pbix")
ontology = extractor.extract().to_ontology() # 70% auto-generated!
ontology.export_fabric_iq("supply_chain_ontology.json") # Ready for AI agentsWhat you get:
- ✅ Extract entities, properties, and relationships from Power BI models
- ✅ Parse DAX formulas into business rules automatically
- ✅ Generate Fabric IQ ontology format for Microsoft Fabric
- ✅ Export to OntoGuard for semantic validation firewalls
- ✅ Detect schema drift (prevents the $4.6M mistake!)
- ✅ Calculate semantic debt across multiple dashboards
- ✅ Create semantic contracts for AI agents
pip install pbi-ontology-extractorOr install from source:
git clone https://github.com/cloudbadal007/powerbi-ontology-extractor.git
cd powerbi-ontology-extractor
pip install -e .from powerbi_ontology import PowerBIExtractor, OntologyGenerator
# Step 1: Extract semantic model from Power BI
extractor = PowerBIExtractor("path/to/your/dashboard.pbix")
semantic_model = extractor.extract()
# Step 2: Generate formal ontology
generator = OntologyGenerator(semantic_model)
ontology = generator.generate()
print(f"✅ Extracted {len(ontology.entities)} entities")
print(f"✅ Generated {len(ontology.business_rules)} business rules")
# Step 3: Export to your preferred format
from powerbi_ontology.export import FabricIQExporter, OntoGuardExporter
fabric_exporter = FabricIQExporter(ontology)
fabric_json = fabric_exporter.export()
ontoguard_exporter = OntoGuardExporter(ontology)
ontoguard_json = ontoguard_exporter.export()Scenario: Supply chain dashboard with 500K shipments
# Extract from Power BI
extractor = PowerBIExtractor("Supply_Chain_Operations.pbix")
model = extractor.extract()
# Found:
# - 5 entities (Shipment, Customer, Warehouse, IoTSensor, ComplianceRule)
# - 8 relationships
# - 12 DAX measures (High Risk Shipments, At-Risk Revenue, etc.)
# Generate ontology
ontology = OntologyGenerator(model).generate()
# Business rules extracted automatically from DAX:
# - "High Risk" = Temperature > 25 OR Vibration > 5
# - "At-Risk Customer" = RiskScore > 80 AND has delayed shipments
# Add the missing 30% (business analyst input):
from powerbi_ontology.ontology_generator import BusinessRule
ontology.add_business_rule(BusinessRule(
name="RerouteApproval",
entity="Shipment",
condition="RiskScore > 80",
action="RerouteShipment",
description="High-risk shipments require manager approval for rerouting"
))
# Create schema bindings (PREVENT THE $4.6M MISTAKE!)
from powerbi_ontology import SchemaMapper
mapper = SchemaMapper(ontology, data_source="azure_sql")
binding = mapper.create_binding("Shipment", "dbo.shipments")
# Validate and detect drift
current_schema = {
"shipment_id": "GUID",
"warehouse_location": "String", # Critical column!
"temperature": "Decimal"
}
drift = mapper.detect_drift(binding, current_schema)
if drift.severity == "CRITICAL":
print(f"🚨 DRIFT DETECTED: {drift.message}")
print("This would have caused the $4.6M mistake!")
# Export for AI agents
from powerbi_ontology.export import FabricIQExporter
import json
fabric_exporter = FabricIQExporter(ontology)
fabric_json = fabric_exporter.export()
with open("supply_chain_ontology.json", "w") as f:
json.dump(fabric_json, f, indent=2)Result: Your Power BI dashboard is now an AI-ready ontology!
flowchart LR
A[Power BI .pbix] --> B[PBIX Reader]
B --> C[Semantic Model]
C --> D[DAX Parser]
C --> E[Ontology Generator]
D --> E
E --> F[Formal Ontology]
F --> G1[Fabric IQ]
F --> G2[OntoGuard]
F --> G3[OWL/RDF]
F --> G4[JSON Schema]
F --> H[Schema Mapper]
F --> I[Contract Builder]
H --> J[Drift Detection]
I --> K[AI Agents]
style F fill:#90EE90
style A fill:#FFE4B5
style J fill:#FFB6C1
style K fill:#87CEEB
- ✅ Reads Power BI .pbix files (ZIP-based format)
- ✅ Extracts tables, columns, relationships, hierarchies
- ✅ Parses DAX measures and calculated columns
- ✅ Identifies primary keys and foreign keys
- ✅ Captures descriptions and annotations
- ✅ Extracts row-level security (RLS) rules
- ✅ Parses DAX formulas automatically
- ✅ Extracts conditional logic (IF, SWITCH)
- ✅ Converts CALCULATE filters to business rules
- ✅ Identifies dependencies and relationships
- ✅ Classifies measure types (aggregation, conditional, time intelligence)
- ✅ Entities from tables
- ✅ Properties from columns (with data types)
- ✅ Relationships from foreign keys (with cardinality)
- ✅ Business rules from DAX measures
- ✅ Constraints from data validation
- ✅ Pattern detection (date tables, dimensions, facts)
- ✅ Fabric IQ: Ready for Microsoft Fabric deployment
- ✅ OntoGuard: Semantic validation firewall format
- ✅ OWL/RDF: Standard semantic web format
- ✅ JSON Schema: Universal validation format
- ✅ Validates schema bindings
- ✅ Detects column renames/deletions
- ✅ Alerts when data sources change
- ✅ Prevents AI agents from breaking
- ✅ Suggests fixes for detected drift
- ✅ Analyzes multiple Power BI dashboards
- ✅ Detects conflicting definitions
- ✅ Calculates reconciliation costs ($50K per conflict)
- ✅ Suggests canonical definitions
- ✅ Generates HTML consolidation reports
- ✅ Define read/write/execute permissions
- ✅ Add business rules to contracts
- ✅ Create validation constraints
- ✅ Export contracts for agent deployment
- ✅ Entity-relationship diagrams (matplotlib)
- ✅ Interactive graphs (plotly)
- ✅ Mermaid diagram export
- ✅ Export to PNG, SVG, PDF
# Extract ontology
pbi-ontology extract dashboard.pbix --output ontology.json
# Analyze multiple dashboards
pbi-ontology analyze *.pbix --report semantic_debt.html
# Export to different formats
pbi-ontology export ontology.json --format fabric-iq --output fabric.json
pbi-ontology export ontology.json --format ontoguard --output ontoguard.json
# Validate schema bindings
pbi-ontology validate ontology.json --schema database_schema.json
# Visualize ontology
pbi-ontology visualize ontology.json --output diagram.png --interactive
# Batch process
pbi-ontology batch --input-dir ./dashboards/ --output-dir ./ontologies/- 📖 Getting Started Guide - Installation and quick start
- 📖 Power BI Semantic Models Explained - Understanding .pbix structure
- 📖 Ontology Format Specification - Ontology structure and definitions
- 📖 Fabric IQ Integration Guide - Exporting to Microsoft Fabric
- 📖 Use Cases & Examples - Real-world scenarios
- 📖 API Reference - Complete API documentation
Extract ontology from supply chain dashboards → Deploy AI agents for real-time monitoring → Prevent $4.6M mistakes with schema drift detection
Extract customer risk definitions → Create unified ontology → Deploy AI agents with semantic contracts → Monitor risk in real-time
Extract financial dashboards → Detect semantic conflicts → Calculate semantic debt → Consolidate definitions → Reduce reconciliation costs
Analyze all Power BI dashboards → Identify duplicate logic → Suggest canonical definitions → Reduce semantic debt by $600K+
Extract ontologies → Create semantic contracts → Deploy AI agents → Monitor with OntoGuard → Prevent failures
from powerbi_ontology.export import FabricIQExporter
import json
exporter = FabricIQExporter(ontology)
fabric_json = exporter.export()
# Save and import into Fabric workspace
with open("ontology.json", "w") as f:
json.dump(fabric_json, f, indent=2)
# Deploy as Ontology Item to OneLakefrom powerbi_ontology.export import OntoGuardExporter
import json
exporter = OntoGuardExporter(ontology)
ontoguard_json = exporter.export()
# Use with github.com/cloudbadal007/ontoguard-ai
# Prevents schema drift and AI agent failures
with open("ontoguard_config.json", "w") as f:
json.dump(ontoguard_json, f, indent=2)from powerbi_ontology import ContractBuilder
# Create semantic contract
contract_builder = ContractBuilder(ontology)
contract = contract_builder.build_contract(
agent_name="SupplyChainMonitor",
permissions={
"read": ["Shipment", "Customer"],
"write": {"Shipment": ["Status"]},
"execute": ["RerouteShipment"]
}
)
# Export contract for MCP
contract_json = contract_builder.export_contract(contract, "json")
# Use with github.com/cloudbadal007/universal-agent-connectorThis project implements the concepts from my Medium article series:
- The Power BI Ontology Paradox - Why Power BI models are hidden ontologies and how to unlock them
- Microsoft vs Palantir: Two Paths to Enterprise Ontology - Strategic comparison of ontology approaches
- OntoGuard: Building a Semantic Firewall - Preventing the $4.6M mistake with schema drift detection
- Universal Agent Connector: MCP + Ontology - Production AI infrastructure with semantic contracts
We welcome contributions! See CONTRIBUTING.md for guidelines.
Ways to contribute:
- 🐛 Report bugs via GitHub Issues
- 💡 Suggest features via Feature Requests
- 📝 Improve documentation - Fix typos, add examples, clarify concepts
- 🔧 Submit pull requests - Fix bugs, add features, improve code
- ⭐ Star the repository - Help others discover this project
- 📢 Share with your network - Spread the word about unlocking Power BI ontologies
# Clone repository
git clone https://github.com/cloudbadal007/powerbi-ontology-extractor.git
cd powerbi-ontology-extractor
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install -e .
# Run tests
pytest
# Format code
black powerbi_ontology/ tests/
isort powerbi_ontology/ tests/# Run all tests
pytest
# Run with coverage
pytest --cov=powerbi_ontology --cov-report=html
# Run specific test file
pytest tests/test_extractor.py -v- ✅ Core extraction - Fully implemented
- ✅ DAX parsing - Fully implemented
- ✅ Ontology generation - Fully implemented
- ✅ Schema drift detection - Fully implemented
- ✅ Multi-format export - Fully implemented
- ✅ CLI tool - Fully implemented
- ✅ Visualization - Fully implemented
- 🔄 Test coverage - In progress (aiming for >90%)
- 🔄 Documentation - Continuously improving
- Inspired by Microsoft's Fabric IQ and semantic layer approach
- Built with feedback from the enterprise AI community
- Special thanks to all contributors and early adopters
- Powered by the open-source community
This project is licensed under the MIT License - see the LICENSE file for details.
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📧 Email: cloudpankaj@example.com
- 🐦 Twitter/X: @cloudpankaj
- 💼 LinkedIn: Pankaj Kumar
- 📝 Medium: @cloudpankaj
Built with ❤️ by Pankaj Kumar
If this project helps you unlock the hidden ontologies in your Power BI dashboards, consider sponsoring ☕
Star ⭐ this repo if you find it useful!
- Enhanced DAX parsing for complex formulas
- Power BI Service API integration
- Real-time ontology updates
- GraphQL endpoint for ontologies
- Visual ontology editor
- Automated testing with sample .pbix files
- Performance optimizations for large models
- Multi-language support
Ready to unlock the semantic intelligence in your Power BI dashboards? 🚀
pip install pbi-ontology-extractor
