.. sidebar::

    .. list-table:: **Ontology Card**
       :header-rows: 0

       * - **Domain**
         - Industry
       * - **Category**
         - Building Information
       * - **Current Version**
         - 0.0.1
       * - **Last Updated**
         - 02/23/2023
       * - **Creator**
         - Google
       * - **License**
         - Apache 2.0
       * - **Format**
         - rdf
       * - **Download**
         - `Download Digital Buildings Ontology (DBO) <https://github.com/google/digitalbuildings?tab=readme-ov-file>`_

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. DBO provides a semantic model for describing building assets, their locations, types, operational states, and relationships, supporting digital twins and smart building applications. The ontology enables integration of data from building management systems, IoT devices, and facility management platforms, facilitating automated monitoring, control, and analytics. DBO is designed to be extensible and interoperable, allowing organizations to adapt the ontology to their specific building types and operational requirements. By providing standardized terms and relationships, DBO supports data-driven decision-making, energy optimization, and predictive maintenance in digital buildings. The ontology is open source and maintained by a community of contributors, ensuring ongoing development and alignment with industry needs.

**Example Usage**:
Annotate a smart building system with DBO terms to describe HVAC equipment, lighting systems, sensors, and their spatial locations, enabling automated control and integration with building management platforms.

Metrics & Statistics
--------------------------

.. tab:: Graph


    .. list-table:: Graph Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Total Nodes**
          - 13152
        * - **Total Edges**
          - 32491
        * - **Root Nodes**
          - 1
        * - **Leaf Nodes**
          - 686
    ::


.. tab:: Coverage


    .. list-table:: Knowledge Coverage Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Classes**
          - 3032
        * - **Individuals**
          - 35
        * - **Properties**
          - 7

    ::

.. tab:: Hierarchy


    .. list-table:: Hierarchical Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Depth**
          - 3
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 1.57
        * - **Depth Variance**
          - 0.82
    ::


.. tab:: Breadth


    .. list-table:: Breadth Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Breadth**
          - 3
        * - **Minimum Breadth**
          - 1
        * - **Average Breadth**
          - 1.75
        * - **Breadth Variance**
          - 0.69
    ::

.. tab:: LLMs4OL


    .. list-table:: LLMs4OL Dataset Statistics
        :widths: 50 50
        :header-rows: 0

        * - **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:

.. code-block:: python

    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