Semantic Web for Earth and Environment Technology Ontology (SWEET)

SWEET is a comprehensive collection of interconnected ontologies designed to enhance discovery and utilization of Earth science data through semantic understanding of web resources and Earth system science concepts. It conceptualizes a knowledge space for Earth system science including orthogonal (cross-cutting) concepts such as space, time, Earth realms (atmosphere, hydrosphere, lithosphere), physical quantities, and units, alongside integrative science knowledge concepts such as phenomena, events, and processes. SWEET is represented in OWL (Web Ontology Language) to enable automated reasoning and semantic interoperability in Earth science research. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across atmospheric science, oceanography, geology, and climate science domains. SWEET facilitates Earth science data discovery and knowledge management by enabling semantic search and automated linking of related datasets and research findings. SWEET (Semantic Web for Earth and Environmental Terminology) is a comprehensive collection of interconnected ontologies designed to improve discovery and use of Earth science data through semantic understanding of web resources and Earth system science concepts [1] [2]. It conceptualizes a knowledge space for Earth system science that includes cross-cutting concepts such as space, time, Earth realms, phenomena, physical quantities, and units, alongside more domain-specific scientific concepts [1] [2]. SWEET is represented in OWL and organized as a highly modular ontology suite, enabling semantic interoperability and automated reasoning in Earth and environmental science applications [1] [2]. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across domains such as atmospheric science, oceanography, geology, and climate science [1] [3]. By providing a shared semantic framework, SWEET supports Earth science data discovery, semantic search, and knowledge management across distributed datasets and services [1] [3].

Example Usage: Annotate a climate or Earth observation dataset with SWEET terms to describe observed phenomena, Earth realm or layer, spatial and temporal context, and relevant physical quantities and units, enabling semantic search, automated linking of related datasets, and interoperable Earth science analysis [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

26249

Total Edges

64544

Root Nodes

244

Leaf Nodes

11866
Knowledge Coverage Statistics

Classes

10240

Individuals

2351

Properties

358
Hierarchical Metrics

Maximum Depth

15

Minimum Depth

0

Average Depth

3.62

Depth Variance

9.36
Breadth Metrics

Maximum Breadth

303

Minimum Breadth

2

Average Breadth

102.81

Breadth Variance

8823.90
LLMs4OL Dataset Statistics

Term Types

2219

Taxonomic Relations

16111

Non-taxonomic Relations

515

Average Terms per Type

11.50

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import SWEET

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

References