Quantities, Units, Dimensions and Values (QUDV)

The Quantities, Units, Dimensions and Values (QUDV) ontology is a formal representation of the SysML QUDV modelLibrary, providing a standardized vocabulary for describing quantities, units, dimensions, and values in scientific and engineering domains. QUDV is specified in UML/SysML class/block diagrams and is designed to support interoperability and alignment with other standardization efforts concerning quantities and units. The ontology enables the semantic annotation of measurement data, supporting unit conversion, dimensional analysis, and data integration across diverse systems. QUDV is used in systems engineering, modeling and simulation, and scientific data management to ensure consistency and comparability of quantitative information. By providing a common framework, QUDV facilitates automated reasoning, validation, and knowledge sharing in multidisciplinary projects. The ontology is maintained by the SysML community and is aligned with other units and measurements ontologies for broader compatibility.

Example Usage: Annotate a systems engineering model with QUDV terms to specify the quantities measured (e.g., “length”, “mass”), their units (e.g., “meter”, “kilogram”), and dimensions, enabling automated unit conversion and validation across engineering tools.

Metrics & Statistics

Graph Statistics

Total Nodes

186

Total Edges

491

Root Nodes

4

Leaf Nodes

20

Knowledge Coverage Statistics

Classes

33

Individuals

0

Properties

25

Hierarchical Metrics

Maximum Depth

21

Minimum Depth

0

Average Depth

8.44

Depth Variance

24.45

Breadth Metrics

Maximum Breadth

17

Minimum Breadth

1

Average Breadth

8.00

Breadth Variance

22.18

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

9

Non-taxonomic Relations

0

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import QUDV

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