Geographical Entities Ontology (GEO)

The Geographical Entities Ontology (GEO) provides a comprehensive inventory and formal representation of geopolitical and geographical entities, including sovereign states, administrative subdivisions, and various geographical regions. GEO distinguishes between political entities (countries, provinces, cities) and natural geographical features (mountains, rivers, seas), enabling precise semantic representation of territorial and regional concepts. The ontology uses hierarchical relationships to model administrative subdivisions and political jurisdictions at multiple levels (national, state, regional, local), supporting complex governance structures. GEO facilitates location-aware data annotation, enabling applications in geopolitical analysis, administrative reporting, and location-based services to unambiguously identify geographic and political entities. The ontology is designed for integration with other geographic and spatial ontologies, supporting linked data applications in geography, governance, and international relations.

Example Usage: Annotate a political news article with GEO terms such as “France” (sovereign state) linked to its subdivisions “Île-de-France” and “Paris” (administrative entities) to enable geographic and political context discovery.

Metrics & Statistics

Graph Statistics

Total Nodes

108572

Total Edges

246406

Root Nodes

298

Leaf Nodes

54170
Knowledge Coverage Statistics

Classes

397

Individuals

46948

Properties

75
Hierarchical Metrics

Maximum Depth

13

Minimum Depth

0

Average Depth

1.91

Depth Variance

3.77
Breadth Metrics

Maximum Breadth

356

Minimum Breadth

2

Average Breadth

95.79

Breadth Variance

17126.60
LLMs4OL Dataset Statistics

Term Types

34653

Taxonomic Relations

430

Non-taxonomic Relations

19

Average Terms per Type

1386.12

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import GEO

ontology = GEO()
ontology.load("path/to/GEO-ontology.owl")

# 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