Automotive Ontology (AUTO)

The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships in the automotive industry. It provides a standardized vocabulary for describing vehicles, components, systems, manufacturing processes, and regulatory requirements. AUTO is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group and follows best practices established by the EDM Council’s Financial Industry Business Ontology (FIBO) Community. The ontology supports interoperability between automotive data sources, enabling integration of information from manufacturers, suppliers, service providers, and regulatory bodies. AUTO facilitates advanced applications such as digital twins, predictive maintenance, supply chain optimization, and regulatory compliance. By providing a common semantic framework, AUTO enables automated reasoning, data analytics, and knowledge sharing across the automotive ecosystem.

Example Usage: Annotate a vehicle information system with AUTO terms to describe vehicle models, engine types, safety features, and maintenance schedules, enabling integration with manufacturer databases and regulatory reporting systems.

Metrics & Statistics

Graph Statistics

Total Nodes

6344

Total Edges

17693

Root Nodes

417

Leaf Nodes

2589
Knowledge Coverage Statistics

Classes

1372

Individuals

58

Properties

336
Hierarchical Metrics

Maximum Depth

25

Minimum Depth

0

Average Depth

4.72

Depth Variance

17.16
Breadth Metrics

Maximum Breadth

574

Minimum Breadth

1

Average Breadth

116.38

Breadth Variance

20295.70
LLMs4OL Dataset Statistics

Term Types

58

Taxonomic Relations

2731

Non-taxonomic Relations

42

Average Terms per Type

3.62

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import AUTO

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