.. sidebar::

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

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Materials
       * - **Current Version**
         - None
       * - **Last Updated**
         - None
       * - **Creator**
         - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro
       * - **License**
         - Creative Commons Attribution 4.0 International (CC BY 4.0)
       * - **Format**
         - ttl
       * - **Download**
         - `Download Open Innovation Environment Models (OIEModels) <https://github.com/emmo-repo/OIE-Ontologies/>`_

Open Innovation Environment Models (OIEModels)
========================================================================================================

The Open Innovation Environment Models (OIEModels) ontology is a domain-level ontology developed to represent models in materials science. It provides a structured vocabulary for describing models, their properties, and data, supporting both experimental and computational research in materials science.

The ontology employs a class-based modeling approach, defining classes for different types of models, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEModels supports the integration of data from various sources, promoting interoperability and data-driven research in materials modeling.

Typical applications of OIEModels include the development of new models with specific properties, the optimization of model properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEModels enhances collaboration and innovation in the field of materials modeling.

**Example Usage**:
Annotate a modeling dataset with OIEModels terms to specify model types, properties, and data, enabling semantic search and integration with materials informatics platforms.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 186
        * - **Total Edges**
          - 413
        * - **Root Nodes**
          - 0
        * - **Leaf Nodes**
          - 64
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 108
        * - **Individuals**
          - 0
        * - **Properties**
          - 1

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 0
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.00
        * - **Depth Variance**
          - 0.00
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 0
        * - **Minimum Breadth**
          - 0
        * - **Average Breadth**
          - 0.00
        * - **Breadth Variance**
          - 0.00
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 101
        * - **Non-taxonomic Relations**
          - 0
        * - **Average Terms per Type**
          - 0.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import OIEModels

    ontology = OIEModels()
    ontology.load("path/to/OIEModels-ontology.ttl")

    # 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