Common Core Ontologies (CCO)

The Common Core Ontologies (CCO) are a suite of eleven interconnected mid-level ontologies that provide logically well-defined generic terms and relations applicable across many domains of interest [1] [2]. CCO extends the Basic Formal Ontology (BFO), an upper-level ontology, and is designed to support semantic interoperability, data integration, and reusable domain ontology development [1].

CCO is built on formal semantic principles, ensuring that its concepts are unambiguous, semantically consistent, and suitable for computational reasoning [2]. The ontology suite covers foundational concepts including objects, processes, qualities, information entities, locations, units of measure, agents, artifacts, facilities, and relations between entities [1] [2]. Its terms are intended to be reused and extended by domain-specific ontologies while preserving compatibility with other CCO- and BFO-based systems [1].

The ontologies are documented with formal definitions, examples, and design patterns that support both human understanding and automated reasoning [2]. CCO can be used in enterprise information systems, knowledge graph construction, semantic data integration, and ontology engineering projects that require rigorous semantic foundations [1] [2].

Example Usage: Represent a business domain ontology by extending CCO’s generic Object, Agent, Organization, and Event/Process concepts to define company-specific entities such as employees, contracts, transactions, departments, and business activities. This helps ensure that the business ontology remains compatible with other systems using CCO or BFO-based semantic foundations [1] [2].

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

References