.. sidebar::

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

       * - **Domain**
         - Scholarly Knowledge
       * - **Category**
         - Computer Science
       * - **Current Version**
         - 3.4
       * - **Last Updated**
         - None
       * - **Creator**
         - Knowledge Media Institute, Open University
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - owl
       * - **Download**
         - `Download Computer Science Ontology (CSO) <https://cso.kmi.open.ac.uk/home>`_

Computer Science Ontology (CSO)
========================================================================================================

The Computer Science Ontology (CSO) is a large-scale semantic resource that provides a comprehensive vocabulary of research areas, topics, and concepts in computer science organized in a hierarchical taxonomy. It covers diverse computing domains including artificial intelligence, software engineering, networking, databases, human-computer interaction, and emerging areas, enabling precise semantic annotation of research contributions. CSO supports sophisticated relationship modeling including superTopicOf (for topic hierarchies), contributesTo (linking topics to solutions), and other domain-relevant relationships enabling knowledge discovery and research mapping. The ontology enables automated research classification, literature organization, and expertise matching by providing standardized semantic definitions of computer science research areas. CSO facilitates semantic interoperability in scholarly information systems, research management platforms, and academic recommendation systems.

**Example Usage**: Annotate a research paper or researcher profile with CSO terms such as "Machine Learning" (main topic), "Deep Learning" (subtopic), "Natural Language Processing" (related topic), and associated research methods and applications to enable semantic discovery of related research.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 25897
        * - **Total Edges**
          - 152243
        * - **Root Nodes**
          - 94
        * - **Leaf Nodes**
          - 11199
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 0
        * - **Individuals**
          - 0
        * - **Properties**
          - 0

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.67
        * - **Depth Variance**
          - 0.22
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 187
        * - **Minimum Breadth**
          - 94
        * - **Average Breadth**
          - 140.50
        * - **Breadth Variance**
          - 2162.25
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 44204
        * - **Non-taxonomic Relations**
          - 49080
        * - **Average Terms per Type**
          - 0.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import CSO

    ontology = CSO()
    ontology.load("path/to/CSO-ontology.owl")

    # 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