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Developing a Data Ontology Framework for an Intelligent Building Management System

Image of Jon Schoenfeld
Jon Schoenfeld

As more buildings become smart, there is a growing need for standardized semantic data models that allows for better data use. The goal of using common data models is to enable a better understanding of a smart building’s data in a continuously changing environment. A robust data ontology framework makes this possible and allows your building management system to become truly intelligent.

Best Practices for Developing a Data Ontology Framework for Building Management System

In the world of building management, there are several standards for semantic modeling and tagging. This multiplicity of standards results in more variations in naming and tagging data models and complicates the real-world application of building data. The discrepancies in naming and tagging can be best addressed through a uniform data and relationship model. 

Some of the best practices for developing a data ontology framework for intelligent buildings include:


Avoid Proprietary Standards

Proprietary data modeling standards make it difficult to integrate equipment and devices from multiple manufacturers into a single BMS platform. In contrast, an open-source data model taxonomy unifies naming and tagging standards to facilitate seamless integration.

Align and Expand Existing Taxonomies

Existing standards serve as good reference points and can be used as the base for tagging schema when developing a data ontology framework. The ontology framework is not about creating a new language—it’s about leveraging the data modeling accomplished through other ontologies. Aligning and expanding existing taxonomies supports the real-world integration requirements in a smart building.

Simplify Ontology

When developing a data ontology framework, simplicity should be a core principle to improve understanding of the type and purpose of building data. Simplified APIs and data tags can be easily and quickly understood both by developers and people configuring a BMS. However, an over-simplified framework can also create limitations and is best suited for a smaller software ecosystem.

Document Data Tagging and Data Modeling

Documenting data tagging standards and how to apply data models is critical for developing a data ontology framework. Documentation creates better understanding of how each data modeling standard works and is implemented. Documentation of each schema also provides clarity on how each option would fit into your tech stack.

Maintain Cross-Compatibility and Convergence

Building subsystems often have partially compatible data models and are unable to communicate with each other over signaling and protocols. A data ontology framework should ensure semantic compatibility to facilitate integration or convergence between different subsystems.

An open-source data ontology framework developed according to these best practices can accommodate the ever-increasing number of IoT devices and systems in today’s smart buildings.    

The Ontology Alignment Project

The Ontology Alignment Project (OAP) was developed by Buildings IOT as an open-source taxonomy. It uses unique codes based on Project Haystack to model building equipment and systems, including HVAC, lighting, and IoT devices. The OAP opens up new opportunities to track data across building systems for fault detection and diagnostics (FDD), user interfaces, analytics, and other use cases. 

Unlike other naming and tagging conventions, the OAP offers:

  • Alignment across existing taxonomies: OAP uses Haystack as the primary basis for the tagging schema and expands on existing taxonomies. This data ontology framework focuses on bringing together different naming and tagging standards and enabling standardization through automatic translation between other ontologies.

  • Availability through API: OAP is an API. This means OAP definitions can be pulled through the API in real-time. The GraphQL API makes it easy for users to build applications that are interconnected with the OAP’s data model. 

  • Addition of equipment: During the integration stage of new builds, equipment, points, and other entities can be tagged based on the OPA standards. For existing buildings, the data model can be applied to building systems at any time, adding and tagging more equipment as building systems scale up. 

  • Usefulness over usability: The OAP lets building control contractors and integrators deliver intelligent BMSs more consistently. As the data model can be easily understood by intelligent building management platforms without significant investment in custom solutions, users can get meaningful insights into maintenance and commissioning. The ontology framework is not just usable but useful for future-proofing building systems. 

The OAP has been successfully implemented in the 800 Fulton Project in Chicago. The Buildings IOT team used the OAP standards to model the data in all connected building systems at the property comprising 85 facilities with 18 different types of BMS, 11,000 equipment, and 160,000 BMS points. All of the tagged data is accessible from one single intelligent building platform—onPoint.    

Buildings IOT offers the state-of-the-art services and products you need to develop a data ontology framework for your smart building. Contact our team of experts to learn more about what we can do for you.



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