.. sidebar::

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

       * - **Domain**
         - Social Sciences
       * - **Category**
         - Social Networks
       * - **Current Version**
         - 1.36
       * - **Last Updated**
         - 2018/02/28
       * - **Creator**
         - Data Science Institute, NUI Galway
       * - **License**
         - Creative Commons 3.0
       * - **Format**
         - rdf
       * - **Download**
         - `Download Semantically-Interlinked Online Communities (SIOC) <http://rdfs.org/sioc/spec/>`_

Semantically-Interlinked Online Communities (SIOC)
========================================================================================================

The SIOC (Semantically-Interlinked Online Communities) Ontology is a widely used ontology for describing the information and structure of online communities. It provides a standardized vocabulary for representing discussion forums, blogs, wikis, social networks, and other collaborative platforms. SIOC enables the modeling of users, posts, threads, topics, and relationships between community members and content. By providing a common framework, SIOC facilitates interoperability between social platforms, supports data integration, and enables advanced queries and analytics on social data. The ontology is used in social media mining, digital humanities, and knowledge graph construction to link and analyze user-generated content across platforms. SIOC is actively maintained and extended to support emerging social web technologies and applications.

**Example Usage**:
Annotate a forum or blog platform with SIOC terms to describe users, posts, threads, and relationships, enabling semantic search and cross-platform analysis of online community interactions.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 230
        * - **Total Edges**
          - 551
        * - **Root Nodes**
          - 0
        * - **Leaf Nodes**
          - 123
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 14
        * - **Individuals**
          - 0
        * - **Properties**
          - 91

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 0
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.00
        * - **Depth Variance**
          - 0.00
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 0
        * - **Minimum Breadth**
          - 0
        * - **Average Breadth**
          - 0.00
        * - **Breadth Variance**
          - 0.00
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 9
        * - **Non-taxonomic Relations**
          - 31
        * - **Average Terms per Type**
          - 0.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import SIOC

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