.. sidebar::

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

       * - **Domain**
         - Geography
       * - **Category**
         - Geographic Knowledge
       * - **Current Version**
         - None
       * - **Last Updated**
         - 2019-02-17
       * - **Creator**
         - William R Hogan
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - owl
       * - **Download**
         - `Download Geographical Entities Ontology (GEO) <https://github.com/mcwdsi/geographical-entity-ontology/blob/master/geo-all.owl>`_

Geographical Entities Ontology (GEO)
========================================================================================================

The Geographical Entities Ontology (GEO) provides a comprehensive inventory and formal representation of geopolitical and geographical entities, including sovereign states, administrative subdivisions, and various geographical regions. GEO distinguishes between political entities (countries, provinces, cities) and natural geographical features (mountains, rivers, seas), enabling precise semantic representation of territorial and regional concepts. The ontology uses hierarchical relationships to model administrative subdivisions and political jurisdictions at multiple levels (national, state, regional, local), supporting complex governance structures. GEO facilitates location-aware data annotation, enabling applications in geopolitical analysis, administrative reporting, and location-based services to unambiguously identify geographic and political entities. The ontology is designed for integration with other geographic and spatial ontologies, supporting linked data applications in geography, governance, and international relations.

**Example Usage**: Annotate a political news article with GEO terms such as "France" (sovereign state) linked to its subdivisions "Île-de-France" and "Paris" (administrative entities) to enable geographic and political context discovery.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 108572
        * - **Total Edges**
          - 246406
        * - **Root Nodes**
          - 298
        * - **Leaf Nodes**
          - 54170
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 397
        * - **Individuals**
          - 46948
        * - **Properties**
          - 75

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 13
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 1.91
        * - **Depth Variance**
          - 3.77
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 356
        * - **Minimum Breadth**
          - 2
        * - **Average Breadth**
          - 95.79
        * - **Breadth Variance**
          - 17126.60
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 34653
        * - **Taxonomic Relations**
          - 430
        * - **Non-taxonomic Relations**
          - 19
        * - **Average Terms per Type**
          - 1386.12
    ::

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

.. code-block:: python

    from ontolearner.ontology import GEO

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