.. sidebar::

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

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Materials Testings
       * - **Current Version**
         - 0.4
       * - **Last Updated**
         - 04/16/2021
       * - **Creator**
         - Markus Schilling, markus.schilling@bam.de; Philipp von Hartrott, philipp.von.hartrott@iwm.fraunhofer.de
       * - **License**
         - Creative Commons Attribution 4.0 International (CC BY 4.0)
       * - **Format**
         - rdf
       * - **Download**
         - `Download Matolab Tensile Test Ontology (MOL_TENSILE) <https://matportal.org/ontologies/MOL_TENSILE>`_

Matolab Tensile Test Ontology (MOL_TENSILE)
========================================================================================================


The Matolab Tensile Test Ontology (MOL_TENSILE) is a domain ontology developed to describe the tensile test process. It provides a structured vocabulary for representing the testing methods, equipment, and results, supporting both experimental and computational research in materials testing.

The ontology employs a class-based modeling approach, defining classes for different types of tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MOL_TENSILE supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in materials testing.

Typical applications of MOL_TENSILE include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MOL_TENSILE enhances collaboration and innovation in the field of materials testing.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 1970
        * - **Total Edges**
          - 3602
        * - **Root Nodes**
          - 132
        * - **Leaf Nodes**
          - 1245
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 371
        * - **Individuals**
          - 20
        * - **Properties**
          - 95

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 90
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 16.30
        * - **Depth Variance**
          - 665.10
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 285
        * - **Minimum Breadth**
          - 2
        * - **Average Breadth**
          - 11.49
        * - **Breadth Variance**
          - 1763.74
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 20
        * - **Taxonomic Relations**
          - 370
        * - **Non-taxonomic Relations**
          - 20
        * - **Average Terms per Type**
          - 6.67
    ::

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

.. code-block:: python

    from ontolearner.ontology import MOLTENSILE

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

Annotate a tensile testing dataset with MOL_TENSILE terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms.