PROcess Chemistry Ontology (PROCO)

PROCO (PROcess Chemistry Ontology) is a formal ontology developed to standardize the representation of entities, relationships, and processes in process chemistry and chemical manufacturing [1]. It provides a structured vocabulary for describing chemical reactions, reactants, products, catalysts, reaction conditions, and process steps used in laboratory and industrial chemistry workflows [1]. The ontology captures important process chemistry concepts such as temperature, pressure, time, stirring, solvents, workup operations, and other process-related information needed for detailed representation of chemical synthesis workflows [1]. By providing explicit and machine-interpretable definitions, PROCO supports semantic interoperability, data integration, and reasoning across process chemistry databases, laboratory information systems, and process development workflows [1].

Example Usage: Represent a multi-step synthesis process with PROCO terms for each reaction step, including reactants, catalysts, solvents, reaction conditions such as temperature and pressure, workup procedures, and desired products with yield information, enabling semantic integration, process comparison, and automated reasoning across process chemistry datasets [1].

Metrics & Statistics

Graph Statistics

Total Nodes

6258

Total Edges

11796

Root Nodes

89

Leaf Nodes

4646
Knowledge Coverage Statistics

Classes

970

Individuals

14

Properties

61
Hierarchical Metrics

Maximum Depth

15

Minimum Depth

0

Average Depth

3.35

Depth Variance

8.54
Breadth Metrics

Maximum Breadth

228

Minimum Breadth

1

Average Breadth

60.19

Breadth Variance

4521.40
LLMs4OL Dataset Statistics

Term Types

14

Taxonomic Relations

1757

Non-taxonomic Relations

1

Average Terms per Type

7.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import PROCO

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