Quantities, Units, Dimensions and Data Types (QUDT)

The Quantities, Units, Dimensions and Data Types (QUDT) ontology is a comprehensive framework for representing quantities, units, dimensions, and data types in scientific, engineering, and technical domains. QUDT provides a standardized vocabulary for describing measurement units, physical quantities, conversion factors, and dimensional analysis, supporting data expressed in RDF and JSON. The ontology is widely used for semantic annotation of scientific datasets, IoT data streams, and engineering models, enabling automated unit conversion, validation, and interoperability across diverse systems. QUDT is maintained by NASA Ames Research Center and is continuously updated to reflect new standards and measurement systems. By providing a common semantic foundation, QUDT facilitates data integration, analytics, and knowledge sharing in multidisciplinary projects. The ontology is extensible and can be aligned with other units and measurements ontologies for broader compatibility.

Example Usage: Annotate a scientific dataset with QUDT terms to specify the quantities measured (e.g., “temperature”, “pressure”), their units (e.g., “degree Celsius”, “pascal”), and conversion factors, enabling automated unit conversion and semantic search across datasets.

Metrics & Statistics

Graph Statistics

Total Nodes

772

Total Edges

2288

Root Nodes

0

Leaf Nodes

233

Knowledge Coverage Statistics

Classes

73

Individuals

24

Properties

165

Hierarchical Metrics

Maximum Depth

0

Minimum Depth

0

Average Depth

0.00

Depth Variance

0.00

Breadth Metrics

Maximum Breadth

0

Minimum Breadth

0

Average Breadth

0.00

Breadth Variance

0.00

LLMs4OL Dataset Statistics

Term Types

27

Taxonomic Relations

400

Non-taxonomic Relations

12

Average Terms per Type

2.45

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import QUDT

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