Quantities, Units, Dimensions and Values (QUDV)

The Quantities, Units, Dimensions and Values (QUDV) ontology is a formal representation of the SysML QUDV model library, providing a standardized framework for describing quantities, units, dimensions, and values in scientific and engineering domains [1]. The QUDV OWL representation was created to support ontology-based use of the SysML QUDV model and to enable alignment with other standardization efforts concerning quantities and units [1]. QUDV supports the description of quantity kinds, measurement units, dimensions, values, and related measurement concepts, enabling quantitative information to be represented in a machine-readable form [2]. The ontology enables semantic annotation of measurement data, supporting unit representation, dimensional analysis, validation, and data integration across engineering and scientific systems [1] [2]. It is useful in systems engineering, modeling and simulation, sensor data annotation, and scientific data management where consistent representation of units, dimensions, and quantities is required [2]. By providing a common conceptual framework, QUDV facilitates interoperability, automated reasoning, validation, and knowledge sharing across multidisciplinary engineering and semantic web applications [1] [2].

Example Usage: Annotate a systems engineering model with QUDV terms to specify measured quantities such as length and mass, their units such as meter and kilogram, dimensions, and associated values. This enables unit-aware data integration, dimensional validation, and interoperability across engineering and semantic web tools [1] [2].

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

References