This repository presents a conceptual enterprise knowledge graph designed as a digital twin for a modern residential complex, focusing on building hierarchy, automation systems, predictive maintenance, security, and community facilities.
Digital twins are virtual representations of physical assets and environments that support monitoring, analysis, and decision-making.
In the built environment, digital twins enable:
- Visualization of structural hierarchies
- Monitoring of equipment and facilities
- Predictive maintenance using sensor-driven insights
- Security and access management
- Integrated community and facility operations
This project models a fictitious residential complex in a metro city, consisting of multiple towers, residential units, shared amenities, automation systems, and security infrastructure.
- A schema-level knowledge graph ontology
- Clearly defined entities, attributes, and relationships
- A high-level ontology and architecture diagram created using Grafo
- Explicit scope boundaries and modeling assumptions
Note:
This repository focuses on conceptual ontology design only.
It does not include a Neo4j instance, real-time IoT data ingestion, or system deployment.
The ontology captures four core dimensions of the residential ecosystem:
- Residential Complex → Towers → Floors → Flats → Rooms → Zones
- Equipment, sensors, alerts, and maintenance tasks
- Intelligent lighting, HVAC systems, elevator management, and pump room monitoring
- Residents, visitors, security desk, and access records
- Biometric access, fire safety systems, and surveillance infrastructure
- Clubhouse, sports areas, banquet hall, library, and lawns
- Marketplace with shops (supermarket, salon, restaurant, pharmacy)
The diagram below represents the high-level ontology and system relationships modeled for the residential complex digital twin.
- Schema-first modeling
- Semantic clarity inspired by RDF-style relationships
- Alignment with real-world building automation and facility management systems
- Explicit scope control to avoid over-modeling
- The residential complex and all associated data are fictitious
- Sensor readings and automation signals are simulated
- This repository represents conceptual design, not a deployed system
- Neo4j schema instantiation
- Graph-based maintenance query scenarios
- Integration with GraphRAG-style retrieval