.. sidebar::

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

       * - **Domain**
         - Scholarly Knowledge
       * - **Category**
         - Materials Science
       * - **Current Version**
         - 1.3.1
       * - **Last Updated**
         - 2025-03-10
       * - **Creator**
         - Metadata4Ing Workgroup
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - ttl
       * - **Download**
         - `Download Metadata for Intelligent Engineering (Metadata4Ing) <https://git.rwth-aachen.de/nfdi4ing/metadata4ing/metadata4ing>`_

Metadata for Intelligent Engineering (Metadata4Ing)
========================================================================================================

The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. It provides a structured vocabulary for representing research data, processes, and roles, supporting both theoretical and experimental research in intelligent engineering.

The ontology employs a class-based modeling approach, defining classes for different types of research data, processes, and roles, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. Metadata4Ing supports the integration of data from various sources, promoting interoperability and data-driven research in intelligent engineering.

Typical applications of Metadata4Ing include the development of new research data management methods, the optimization of research data generation processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, Metadata4Ing enhances collaboration and innovation in the field of intelligent engineering.

**Example Usage**:
Annotate a research dataset with Metadata4Ing terms to specify data types, processes, and roles, enabling semantic search and integration with research data management platforms.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 1032
        * - **Total Edges**
          - 1517
        * - **Root Nodes**
          - 109
        * - **Leaf Nodes**
          - 731
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 48
        * - **Individuals**
          - 47
        * - **Properties**
          - 100

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 7
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 1.54
        * - **Depth Variance**
          - 1.36
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 413
        * - **Minimum Breadth**
          - 4
        * - **Average Breadth**
          - 109.75
        * - **Breadth Variance**
          - 18099.19
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 37
        * - **Taxonomic Relations**
          - 44
        * - **Non-taxonomic Relations**
          - 6
        * - **Average Terms per Type**
          - 9.25
    ::

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

.. code-block:: python

    from ontolearner.ontology import Metadata4Ing

    ontology = Metadata4Ing()
    ontology.load("path/to/Metadata4Ing-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