Ontology of Units of Measure (OM)

The Ontology of units of Measure (OM) models concepts and relations important to scientific research, with a strong focus on units, quantities, measurements, measures, and dimensions [1] [2]. It includes common units such as the SI units metre and kilogram, as well as units from other systems of units, including examples such as mile and nautical mile [1]. OM also includes more specialized units and quantities for many scientific and technical application areas, supporting fields such as geometry, mechanics, thermodynamics, electromagnetism, fluid mechanics, chemical physics, astronomy, Earth science, meteorology, material science, microbiology, economics, information technology, and typography [1]. The ontology provides classes, instances, and properties for defining and using measures and units, enabling quantitative research data to be represented more explicitly, integrated across sources, verified, and reproduced [2]. OM supports interoperability by providing a standardized semantic framework for describing quantities, units, dimensions, scales, prefixes, systems of units, and measurement values [1] [2].

The ontology employs a class-based modeling approach, defining classes for different types of units, quantities, and measurements, along with properties to describe their characteristics and relationships [2]. Hierarchies are used to organize units, quantities, dimensions, and systems of units into structured categories, enabling efficient retrieval, comparison, conversion, and analysis [1]. OM supports the integration of quantitative data from diverse sources, promoting interoperability and data-driven research in units and measurements [2].

Typical applications of OM include semantic annotation of scientific datasets, explicit representation of measurement values, support for unit conversion, development of measurement-related tools and services, and integration of heterogeneous datasets for analytics and knowledge discovery [1] [2]. By providing a standardized vocabulary and framework, OM enhances collaboration, reproducibility, and innovation in domains that depend on precise units and measurements [2].

Example Usage: Annotate a scientific dataset with OM terms to specify quantities, units, dimensions, and measurement values, enabling semantic search, unit conversion, data integration, and interoperability with measurement management platforms [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

9352

Total Edges

27500

Root Nodes

27

Leaf Nodes

4836
Knowledge Coverage Statistics

Classes

1100

Individuals

1688

Properties

34
Hierarchical Metrics

Maximum Depth

1

Minimum Depth

0

Average Depth

0.27

Depth Variance

0.20
Breadth Metrics

Maximum Breadth

27

Minimum Breadth

10

Average Breadth

18.50

Breadth Variance

72.25
LLMs4OL Dataset Statistics

Term Types

1953

Taxonomic Relations

1124

Non-taxonomic Relations

0

Average Terms per Type

42.46

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import OM

ontology = OM()
ontology.load("path/to/OM-ontology.rdf")

# 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