Semantic Sensor Network Ontology (SSN)

The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors, observations, procedures, features of interest, samples, observed properties, and actuators [1] [2]. SSN includes a lightweight, self-contained core ontology called SOSA - Sensor, Observation, Sample, and Actuator which provides elementary classes and properties for modelling observation, sampling, and actuation activities [1] [2].

SSN and SOSA support a modular architecture with different scopes and degrees of axiomatization, enabling use across applications such as satellite imagery, scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things [1] [2]. By providing a standardized vocabulary, SSN supports semantic annotation, interoperability, data integration, querying, and reuse of sensor and observation data across sensor-network and data-management platforms [1] [2].

Example Usage: Annotate a sensor network dataset with SSN/SOSA terms to specify sensors, observations, procedures, features of interest, samples, observed properties, actuators, and results, enabling semantic search and integration with sensor-network management and Web of Things platforms [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

551

Total Edges

1643

Root Nodes

22

Leaf Nodes

106

Knowledge Coverage Statistics

Classes

22

Individuals

9

Properties

38

Hierarchical Metrics

Maximum Depth

1

Minimum Depth

0

Average Depth

0.15

Depth Variance

0.13

Breadth Metrics

Maximum Breadth

22

Minimum Breadth

4

Average Breadth

13.00

Breadth Variance

81.00

LLMs4OL Dataset Statistics

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:

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

References