.. sidebar::

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

       * - **Domain**
         - Industry
       * - **Category**
         - Automotive
       * - **Current Version**
         - None
       * - **Last Updated**
         - 2021-03-01
       * - **Creator**
         - EDM Council
       * - **License**
         - MIT
       * - **Format**
         - rdf
       * - **Download**
         - `Download Automotive Ontology (AUTO) <https://github.com/edmcouncil/auto/tree/master>`_

Automotive Ontology (AUTO)
========================================================================================================

The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships in the automotive industry. It provides a standardized vocabulary for describing vehicles, components, systems, manufacturing processes, and regulatory requirements. AUTO is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group and follows best practices established by the EDM Council's Financial Industry Business Ontology (FIBO) Community. The ontology supports interoperability between automotive data sources, enabling integration of information from manufacturers, suppliers, service providers, and regulatory bodies. AUTO facilitates advanced applications such as digital twins, predictive maintenance, supply chain optimization, and regulatory compliance. By providing a common semantic framework, AUTO enables automated reasoning, data analytics, and knowledge sharing across the automotive ecosystem.

**Example Usage**:
Annotate a vehicle information system with AUTO terms to describe vehicle models, engine types, safety features, and maintenance schedules, enabling integration with manufacturer databases and regulatory reporting systems.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 6344
        * - **Total Edges**
          - 17693
        * - **Root Nodes**
          - 417
        * - **Leaf Nodes**
          - 2589
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 1372
        * - **Individuals**
          - 58
        * - **Properties**
          - 336

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 25
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 4.72
        * - **Depth Variance**
          - 17.16
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 574
        * - **Minimum Breadth**
          - 1
        * - **Average Breadth**
          - 116.38
        * - **Breadth Variance**
          - 20295.70
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 58
        * - **Taxonomic Relations**
          - 2731
        * - **Non-taxonomic Relations**
          - 42
        * - **Average Terms per Type**
          - 3.62
    ::

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

.. code-block:: python

    from ontolearner.ontology import AUTO

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