Common Core Ontologies (CCO)

The Common Core Ontologies (CCO) is a comprehensive suite of eleven interconnected ontologies providing logically well-defined generic terms and relations applicable across all domains of interest. CCO is built on formal semantic principles, ensuring that its concepts are unambiguous, semantically consistent, and applicable to diverse knowledge representation tasks. The ontology covers foundational concepts including objects, events, qualities, locations, and abstract entities, with explicit definitions of relationships between them. CCO is designed for maximum reusability across domain ontologies, enabling developers to extend CCO terms for specialized applications while maintaining semantic interoperability. The ontologies are documented with formal definitions, examples, and competency questions supporting both human understanding and computational reasoning. CCO has been adopted in enterprise information systems, knowledge graph construction, and semantic data integration projects requiring rigorous ontological foundations.

Example Usage: Represent a business domain ontology by extending CCO’s generic Object and Event concepts to define company-specific entities (employees, contracts, transactions) and their relationships, ensuring compatibility with other systems using CCO foundations.

Metrics & Statistics

Graph Statistics

Total Nodes

6002

Total Edges

13554

Root Nodes

19

Leaf Nodes

3389
Knowledge Coverage Statistics

Classes

1539

Individuals

350

Properties

277
Hierarchical Metrics

Maximum Depth

10

Minimum Depth

0

Average Depth

4.35

Depth Variance

5.00
Breadth Metrics

Maximum Breadth

56

Minimum Breadth

2

Average Breadth

23.18

Breadth Variance

276.88
LLMs4OL Dataset Statistics

Term Types

362

Taxonomic Relations

1532

Non-taxonomic Relations

21

Average Terms per Type

10.06

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import CCO

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