.. sidebar::

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

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Sensor Networks
       * - **Current Version**
         - 1.0
       * - **Last Updated**
         - 2017-04-17
       * - **Creator**
         - W3C/OGC Spatial Data on the Web Working Group
       * - **License**
         - http://www.w3.org/Consortium/Legal/2015/copyright-software-and-document
       * - **Format**
         - ttl
       * - **Download**
         - `Download Semantic Sensor Network Ontology (SSN) <https://github.com/w3c/sdw-sosa-ssn/tree/482484fe2edc1ba8aa7f19214a72bdb77123e833>`_

Semantic Sensor Network Ontology (SSN)
========================================================================================================

The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given.

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

Typical applications of SSN include the development of new sensor network methods, the optimization of sensor data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SSN enhances collaboration and innovation in the field of sensor networks.

**Example Usage**:
Annotate a sensor network dataset with SSN terms to specify sensor types, observations, and related data, enabling semantic search and integration with sensor network management platforms.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 551
        * - **Total Edges**
          - 1643
        * - **Root Nodes**
          - 22
        * - **Leaf Nodes**
          - 106
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 22
        * - **Individuals**
          - 9
        * - **Properties**
          - 38

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.15
        * - **Depth Variance**
          - 0.13
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 22
        * - **Minimum Breadth**
          - 4
        * - **Average Breadth**
          - 13.00
        * - **Breadth Variance**
          - 81.00
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 93
        * - **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 SSN

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