.. sidebar::

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

       * - **Domain**
         - Web and Internet
       * - **Category**
         - interoperability
       * - **Current Version**
         - 3.2.1
       * - **Last Updated**
         - 2020-12-31
       * - **Creator**
         - ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M)
       * - **License**
         - None
       * - **Format**
         - rdf
       * - **Download**
         - `Download Smart Applications REFerence ontology (SAREF) <https://saref.etsi.org/core/v3.2.1/>`_

Smart Applications REFerence ontology (SAREF)
========================================================================================================

SAREF is a comprehensive suite of interrelated ontologies that defines a shared model of consensus for enabling semantic interoperability across IoT solutions and smart applications from diverse providers and industry sectors. It provides standardized vocabulary for describing smart devices, their capabilities, interactions, and services in Internet of Things (IoT) and smart environments (smart homes, smart buildings, smart cities). SAREF captures essential IoT concepts including devices, sensors, actuators, services, properties, and the relationships between them, facilitating machine-to-machine communication and automation. The SAREF suite is published as open standards by ETSI Technical Committee SmartM2M (Smart Machine-to-Machine communications), ensuring broad industry adoption and compatibility. SAREF enables semantic data spaces by providing common terminology that allows data and services from different IoT platforms and manufacturers to be integrated and understood.

**Example Usage**: Annotate an IoT device deployment with SAREF terms describing the device type (smart thermostat, light bulb, motion sensor), its capabilities (temperature measurement, brightness control), services offered (remote adjustment, scheduling), and relationships to control systems and user preferences.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 804
        * - **Total Edges**
          - 1720
        * - **Root Nodes**
          - 14
        * - **Leaf Nodes**
          - 376
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 129
        * - **Individuals**
          - 10
        * - **Properties**
          - 89

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.07
        * - **Depth Variance**
          - 0.06
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 14
        * - **Minimum Breadth**
          - 1
        * - **Average Breadth**
          - 7.50
        * - **Breadth Variance**
          - 42.25
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 10
        * - **Taxonomic Relations**
          - 88
        * - **Non-taxonomic Relations**
          - 0
        * - **Average Terms per Type**
          - 10.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import SAREF

    ontology = SAREF()
    ontology.load("path/to/SAREF-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