PROcess Chemistry Ontology (PROCO)

PROCO (PROcess Chemistry Ontology) is a formal ontology that standardizes representation of entities, relationships, and processes in process chemistry and chemical manufacturing. It provides comprehensive vocabulary for describing chemical reactions, reaction conditions, reactants, products, catalysts, and process equipment used in industrial and laboratory chemical processes. PROCO captures essential process chemistry concepts including reaction parameters (temperature, pressure, time, stirring), process steps (mixing, heating, separation), material flows, and safety-critical information for chemical synthesis. The ontology enables semantic interoperability across chemical engineering databases, laboratory information management systems (LIMS), and process simulation tools by providing explicit, unambiguous definitions. PROCO supports knowledge integration and reasoning in process chemistry research, enabling automated workflow design, process optimization, and risk assessment.

Example Usage: Represent a multi-step synthesis process with PROCO terms for each reaction step including reactants, reaction conditions (temperature and pressure ranges), catalysts, solvents, workup procedures, and desired products with yield information.

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