Chemical Methods Ontology (ChMO)

The Chemical Methods Ontology (ChMO) is a comprehensive ontology that provides a structured vocabulary for describing chemical methods, experimental techniques, and analytical procedures used in chemistry and related sciences. ChMO contains over 3000 classes covering methods for data collection (e.g., mass spectrometry, electron microscopy), sample preparation and separation (e.g., ionisation, chromatography, electrophoresis), and material synthesis (e.g., epitaxy, vapor deposition). The ontology also describes the instruments and equipment used in these experiments, such as mass spectrometers and chromatography columns, as well as the outputs and results generated. ChMO enables semantic annotation of experimental workflows, facilitating data integration, reproducibility, and advanced analysis across chemical research and laboratory information systems. By providing a standardized framework, ChMO supports interoperability between chemical databases, electronic lab notebooks, and computational tools. The ontology is actively maintained and extended to incorporate new methods and technologies as the field evolves.

Example Usage: Annotate a chemical experiment with ChMO terms to specify the analytical method (e.g., “liquid chromatography-mass spectrometry”), sample preparation steps, instrument configuration, and data outputs, enabling semantic search and integration with other chemical research datasets.

Metrics & Statistics

Graph Statistics

Total Nodes

24075

Total Edges

44651

Root Nodes

3100

Leaf Nodes

17250
Knowledge Coverage Statistics

Classes

3202

Individuals

0

Properties

27
Hierarchical Metrics

Maximum Depth

7

Minimum Depth

0

Average Depth

1.49

Depth Variance

0.63
Breadth Metrics

Maximum Breadth

13439

Minimum Breadth

1

Average Breadth

2993.88

Breadth Variance

20855464.86
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

3601

Non-taxonomic Relations

1

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import ChMO

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