Provenance Knowledge Ontology (PKO)

Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact).

The Provenance Knowledge Ontology (PKO) is a domain ontology for representing procedural knowledge and provenance in industrial contexts. PKO formalizes structured processes, procedures, and best practices relevant to both production and services, capturing technical expertise, regulatory compliance, and operational workflows. The ontology enables the modeling of step-by-step procedures, their dependencies, required resources, and associated outcomes, supporting traceability and knowledge transfer. PKO is designed to facilitate the integration of procedural knowledge with provenance data, enabling organizations to track the execution of processes, ensure quality, and support continuous improvement. By providing a standardized vocabulary, PKO enhances interoperability between industrial information systems and supports automation, training, and compliance auditing. The ontology is maintained by industry experts and is aligned with broader provenance and process ontologies for maximum compatibility.

Example Usage: Annotate a manufacturing process with PKO terms to describe each procedural step, required equipment, responsible personnel, and resulting products, enabling automated tracking, quality assurance, and regulatory compliance.

Metrics & Statistics

Graph Statistics

Total Nodes

302

Total Edges

623

Root Nodes

24

Leaf Nodes

141
Knowledge Coverage Statistics

Classes

38

Individuals

8

Properties

93
Hierarchical Metrics

Maximum Depth

2

Minimum Depth

0

Average Depth

0.50

Depth Variance

0.51
Breadth Metrics

Maximum Breadth

24

Minimum Breadth

5

Average Breadth

12.67

Breadth Variance

66.89
LLMs4OL Dataset Statistics

Term Types

8

Taxonomic Relations

11

Non-taxonomic Relations

0

Average Terms per Type

4.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import PKO

ontology = PKO()
ontology.load("path/to/PKO-ontology.rdf")

# 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