.. sidebar::

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

       * - **Domain**
         - Scholarly Knowledge
       * - **Category**
         - Scholarly Communication
       * - **Current Version**
         - 2.8.1
       * - **Last Updated**
         - 2018-02-16
       * - **Creator**
         - Silvio Peroni, David Shotton
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - owl
       * - **Download**
         - `Download Citation Typing Ontology (CiTO) <https://github.com/SPAROntologies/cito/tree/master/docs/current>`_

Citation Typing Ontology (CiTO)
========================================================================================================

The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations,     both factually and rhetorically. It provides a structured vocabulary for describing citation types, relationships, and contexts, supporting both theoretical and experimental research in scholarly communication.

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

Typical applications of CiTO include the development of new citation analysis methods, the optimization of citation practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, CiTO enhances collaboration and innovation in the field of scholarly communication.

**Example Usage**:
Annotate a research paper with CiTO terms to specify citation types, relationships, and contexts, enabling semantic search and integration with scholarly communication platforms.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 312
        * - **Total Edges**
          - 574
        * - **Root Nodes**
          - 11
        * - **Leaf Nodes**
          - 182
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 10
        * - **Individuals**
          - 0
        * - **Properties**
          - 101

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.56
        * - **Depth Variance**
          - 0.25
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 14
        * - **Minimum Breadth**
          - 11
        * - **Average Breadth**
          - 12.50
        * - **Breadth Variance**
          - 2.25
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 9
        * - **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 CiTO

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