BBC Sport Ontology (BBCSport)

The BBC Sport Ontology (BBCSport) is a simple, lightweight ontology for publishing data about competitive sports events [1]. It provides a structured vocabulary for representing the structure of sports tournaments as a series of events, the competing of agents in a competition, the type of discipline involved in an event, and awards associated with competitions [1].

BBCSport is designed for interoperability with more general event ontologies and draws heavily on the Events Ontology [1]. Although it originates from a BBC use case, it is intended to be applicable to a wide range of competitive sports event data publishing use cases [1]. By providing standardized terms for competitions, events, participants, disciplines, and awards, BBCSport supports semantic annotation, linked-data publishing, content discovery, and integration of sports information [1].

Example Usage: Annotate a sports tournament database with BBCSport terms to specify competitions, tournament events, participating teams or athletes, disciplines, and awards, enabling semantic search and integration across sports data publishing platforms [1].

Metrics & Statistics

Graph Statistics

Total Nodes

232

Total Edges

490

Root Nodes

42

Leaf Nodes

115

Knowledge Coverage Statistics

Classes

28

Individuals

40

Properties

47

Hierarchical Metrics

Maximum Depth

3

Minimum Depth

0

Average Depth

1.07

Depth Variance

1.50

Breadth Metrics

Maximum Breadth

42

Minimum Breadth

10

Average Breadth

21.25

Breadth Variance

153.69

LLMs4OL Dataset Statistics

Term Types

40

Taxonomic Relations

25

Non-taxonomic Relations

0

Average Terms per Type

13.33

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import BBCSport

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