Automotive Ontology (AUTO)

The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships for the automotive domain [1]. It provides a standardized semantic framework for representing automotive concepts and supporting interoperability across heterogeneous automotive data sources [1].

AUTO is built upon the auto Schema.org extension developed through the W3C Automotive Ontology Community Group [1] [2]. This underlying automotive vocabulary has also been discussed in the academic literature as an existing semantic resource for representing common vehicle-related concepts, including cars, buses, motorcycles, and associated automotive information [3]. The development of AUTO extends this foundation into a broader OWL-based industry ontology and follows modelling practices used within the EDM Council community [1].

The ontology supports integration of information from automotive manufacturers, suppliers, dealers, service providers, and other automotive data publishers [1] [2]. By providing a common semantic framework, AUTO supports automotive data integration, knowledge sharing, semantic reasoning, and interoperability across the automotive ecosystem [1] [3].

Example Usage: Annotate a vehicle information system with AUTO terms to describe vehicle models, vehicle types, automotive characteristics, and related industry concepts, enabling integration with manufacturer databases, dealer websites, automotive knowledge graphs, and downstream analytics systems [1] [2]. 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

References