Physico-chemical process ontology (REX)

REX is a comprehensive ontology for formal representation of physico-chemical processes, including both microscopic molecular transformations and macroscopic chemical phenomena occurring over time. It provides structured vocabulary for describing processes at different scales: molecular-level processes (involving chemical bonds, molecular rearrangement, electron transfer) and macroscopic processes (phase changes, dissolution, crystallization). REX distinguishes between different process types and captures temporal and causal relationships between processes, enabling precise semantic representation of chemical transformations. The ontology integrates with biological process vocabularies (e.g., Gene Ontology’s biological process namespace) to bridge molecular biochemistry and cellular processes. REX facilitates knowledge integration in chemistry databases, computational chemistry platforms, and systems biology models by providing standardized process definitions.

Example Usage: Represent a multi-step chemical transformation process using REX terms to describe molecular-level processes (e.g., nucleophilic substitution, oxidation) linked together in sequence, with temporal ordering and causal dependencies between elementary steps.

Metrics & Statistics

Graph Statistics

Total Nodes

2461

Total Edges

5630

Root Nodes

356

Leaf Nodes

1457

Knowledge Coverage Statistics

Classes

552

Individuals

0

Properties

6

Hierarchical Metrics

Maximum Depth

6

Minimum Depth

0

Average Depth

1.35

Depth Variance

0.97

Breadth Metrics

Maximum Breadth

978

Minimum Breadth

5

Average Breadth

304.57

Breadth Variance

116930.53

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

953

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 REX

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