Digital Buildings Ontology (DBO)¶
The Digital Buildings Ontology (DBO) is a structured vocabulary developed by Google for representing information about buildings and building-installed equipment [1] [2]. DBO provides a semantic model for describing building assets, physical spaces, equipment, entity types, operational states, fields, units, and relationships in smart-building environments [1] [2]. The ontology supports the integration and consistent representation of heterogeneous building data through a common semantic model and associated validation tooling [1] [3]. DBO is designed to be extensible and applicable across different buildings and equipment configurations, supporting scalable deployment and reuse of building-management applications [2] [3]. By standardizing how building systems and their relationships are represented, DBO supports building data management, monitoring, analytics, and interoperability across smart-building environments [1] [2]. The ontology is open source and maintained through Google’s Digital Buildings repository [1].
Example Usage: Annotate a smart-building system with DBO terms to describe HVAC equipment, lighting systems, sensors, telemetry fields, operational states, and spatial locations, enabling consistent building-data representation, validation, monitoring, and integration with building-management applications [1] [2] [3]. Metrics & Statistics ————————–
Total Nodes |
13152 |
Total Edges |
32491 |
Root Nodes |
1 |
Leaf Nodes |
686 |
Classes |
3032 |
Individuals |
35 |
Properties |
7 |
Maximum Depth |
3 |
Minimum Depth |
0 |
Average Depth |
1.57 |
Depth Variance |
0.82 |
Maximum Breadth |
3 |
Minimum Breadth |
1 |
Average Breadth |
1.75 |
Breadth Variance |
0.69 |
Term Types |
35 |
Taxonomic Relations |
18738 |
Non-taxonomic Relations |
12 |
Average Terms per Type |
2.06 |
Usage Example¶
Use the following code to import this ontology programmatically:
from ontolearner.ontology import DBO
ontology = DBO()
ontology.load("path/to/DBO-ontology.rdf")
# Extract datasets
data = ontology.extract()
# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations