.. sidebar::

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

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Materials Science
       * - **Current Version**
         - 0.2
       * - **Last Updated**
         - None
       * - **Creator**
         - REACT project team
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - owl
       * - **Download**
         - `Download The Heat Pump Ontology (HPOnt) <https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html/>`_

The Heat Pump Ontology (HPOnt)
========================================================================================================

The Heat Pump Ontology (HPOnt) is a domain ontology developed to formalize and represent all relevant information about heat pumps, including their components, operational parameters, performance metrics, and integration with building energy systems. HPOnt provides a structured vocabulary for describing heat pump types, working fluids, control strategies, installation contexts, and maintenance procedures. The ontology supports semantic annotation of heat pump data, enabling interoperability between smart building systems, energy management platforms, and research databases. HPOnt is designed for extensibility and can be adapted to represent new heat pump technologies, regulatory requirements, and sustainability metrics. By providing a standardized framework, HPOnt facilitates advanced analytics, predictive maintenance, and optimization of heat pump operation in residential, commercial, and industrial settings. The ontology is actively maintained and extended as part of the REACT project and is aligned with European Union research and innovation initiatives.

**Example Usage**:
Annotate a smart building energy management system with HPOnt terms to specify the types of heat pumps installed, their operational parameters (e.g., COP, setpoints), maintenance schedules, and integration with renewable energy sources, enabling semantic search and optimization of building energy performance.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 84
        * - **Total Edges**
          - 143
        * - **Root Nodes**
          - 16
        * - **Leaf Nodes**
          - 43
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 4
        * - **Individuals**
          - 6
        * - **Properties**
          - 12

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 4
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 1.13
        * - **Depth Variance**
          - 1.98
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 16
        * - **Minimum Breadth**
          - 2
        * - **Average Breadth**
          - 6.00
        * - **Breadth Variance**
          - 27.20
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 5
        * - **Taxonomic Relations**
          - 0
        * - **Non-taxonomic Relations**
          - 0
        * - **Average Terms per Type**
          - 2.50
    ::

Usage Example
----------------
Use the following code to import this ontology programmatically:

.. code-block:: python

    from ontolearner.ontology import HPOnt

    ontology = HPOnt()
    ontology.load("path/to/HPOnt-ontology.owl")

    # 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