pbi-ontology-extractor 0.1.5

Extract semantic intelligence from Power BI .pbix files and convert to formal ontologies

PowerBI Ontology Extractor

Transform 20 million Power BI dashboards into AI-ready ontologies

Installation • Quick Start • Documentation • Examples • Contributing

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🎯 The Problem

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

💡 The Solution

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 agents

What 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

🚀 Quick Start

Installation

pip install pbi-ontology-extractor

Or install from source:

git clone https://github.com/cloudbadal007/powerbi-ontology-extractor.git
cd powerbi-ontology-extractor
pip install -e .

Basic Usage

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()

📊 Real-World Example

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!

🎨 Architecture

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

🔥 Key Features

1. Automatic Extraction

• ✅ 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

2. DAX to Business 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)

3. Ontology Generation (70% Automated)

• ✅ 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)

4. Multi-Format Export

• ✅ Fabric IQ: Ready for Microsoft Fabric deployment
• ✅ OntoGuard: Semantic validation firewall format
• ✅ OWL/RDF: Standard semantic web format
• ✅ JSON Schema: Universal validation format

5. Schema Drift Detection (Prevents $4.6M Mistakes!)

• ✅ Validates schema bindings
• ✅ Detects column renames/deletions
• ✅ Alerts when data sources change
• ✅ Prevents AI agents from breaking
• ✅ Suggests fixes for detected drift

6. Semantic Debt Analysis

• ✅ Analyzes multiple Power BI dashboards
• ✅ Detects conflicting definitions
• ✅ Calculates reconciliation costs ($50K per conflict)
• ✅ Suggests canonical definitions
• ✅ Generates HTML consolidation reports

7. Semantic Contracts for AI Agents

• ✅ Define read/write/execute permissions
• ✅ Add business rules to contracts
• ✅ Create validation constraints
• ✅ Export contracts for agent deployment

8. Visualization

• ✅ Entity-relationship diagrams (matplotlib)
• ✅ Interactive graphs (plotly)
• ✅ Mermaid diagram export
• ✅ Export to PNG, SVG, PDF

9. CLI Tool for Automation

# 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/

📚 Documentation

• 📖 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

💼 Use Cases

1. Supply Chain Optimization

Extract ontology from supply chain dashboards → Deploy AI agents for real-time monitoring → Prevent $4.6M mistakes with schema drift detection

2. Customer Risk Management

Extract customer risk definitions → Create unified ontology → Deploy AI agents with semantic contracts → Monitor risk in real-time

3. Financial Reconciliation

Extract financial dashboards → Detect semantic conflicts → Calculate semantic debt → Consolidate definitions → Reduce reconciliation costs

4. Cross-Department Consolidation

Analyze all Power BI dashboards → Identify duplicate logic → Suggest canonical definitions → Reduce semantic debt by $600K+

5. AI Agent Deployment

Extract ontologies → Create semantic contracts → Deploy AI agents → Monitor with OntoGuard → Prevent failures

🔗 Integration with Other Tools

Microsoft Fabric IQ

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 OneLake

OntoGuard (Semantic Firewall)

from 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)

Universal Agent Connector (MCP)

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-connector

📖 Related Articles

This project implements the concepts from my Medium article series:

1. The Power BI Ontology Paradox - Why Power BI models are hidden ontologies and how to unlock them
2. Microsoft vs Palantir: Two Paths to Enterprise Ontology - Strategic comparison of ontology approaches
3. OntoGuard: Building a Semantic Firewall - Preventing the $4.6M mistake with schema drift detection
4. Universal Agent Connector: MCP + Ontology - Production AI infrastructure with semantic contracts

🤝 Contributing

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

Development Setup

# 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/

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=powerbi_ontology --cov-report=html

# Run specific test file
pytest tests/test_extractor.py -v

📊 Project Status

• ✅ 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

🙏 Acknowledgments

• 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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Contact & Support

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: [e-mail]
• 🐦 Twitter/X: @cloudpankaj
• 💼 LinkedIn: Pankaj Kumar
• 📝 Medium: @cloudpankaj

⭐ Star History

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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!

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🎯 Roadmap

• 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

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Ready to unlock the semantic intelligence in your Power BI dashboards? 🚀

pip install pbi-ontology-extractor