Environmental Noise Measurement Ontology (ENM)

The Environmental Noise Measurement Ontology (ENM) is an application ontology developed to support toxicological data management and nanomaterial safety assessment for engineered nanomaterials [1] [2]. It provides a structured vocabulary for describing nanomaterials, experimental procedures, measurement techniques, material properties, exposure scenarios, biological interactions, and safety-related information [1] [2].

ENM was developed in the context of the eNanoMapper project and reuses or extends existing ontologies relevant to nanosafety, including the Nanoparticle Ontology (NPO), Chemical Information Ontology (CHEMINF), Chemical Entities of Biological Interest (ChEBI), and Environment Ontology (ENVO) [2]. The ontology supports semantic annotation, data integration, interoperability, search, and reuse of nanomaterial safety data across research projects and databases [1] [2].

Example Usage: Annotate a nanotoxicology study with ENM terms to specify the nanomaterial type, measurement method, exposure condition, experimental assay, material property, and observed biological effect, enabling cross-study comparison and integration with nanosafety databases [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

102719

Total Edges

226566

Root Nodes

11156

Leaf Nodes

64025
Knowledge Coverage Statistics

Classes

26142

Individuals

9

Properties

53
Hierarchical Metrics

Maximum Depth

130

Minimum Depth

0

Average Depth

1.74

Depth Variance

56.70
Breadth Metrics

Maximum Breadth

11156

Minimum Breadth

1

Average Breadth

206.70

Breadth Variance

1767707.90
LLMs4OL Dataset Statistics

Term Types

9

Taxonomic Relations

36933

Non-taxonomic Relations

84

Average Terms per Type

3.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import ENM

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