Procedural Knowledge Ontology (PKO)

2025-03-26

AI Summary

Procedural knowledge (PK) in industrial settings predominantly exists as tacit, experience-based information, hindering its reuse, governance, and integration with AI tools. Method: This paper introduces the first industrial-process-oriented Procedural Knowledge Ontology (PKO), systematically formalizing PK’s conceptual model and execution semantics. PKO integrates cross-industry requirements and reuses/extends established ontologies—including FOAF and PROV—to ensure semantic interoperability. Built in OWL using a requirements-driven ontology engineering methodology, it supports end-to-end PK lifecycle modeling. Contribution/Results: Evaluated across three heterogeneous industrial scenarios, PKO significantly enhances PK discoverability, reusability, and seamless integration with data-driven analytics tools. It establishes a foundational semantic infrastructure for intelligent industrial knowledge governance and AI-augmented process automation.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Processes, workflows and guidelines are core to ensure the correct functioning of industrial companies: for the successful operations of factory lines, machinery or services, often industry operators rely on their past experience and know-how. The effect is that this Procedural Knowledge (PK) remains tacit and, as such, difficult to exploit efficiently and effectively. This paper presents PKO, the Procedural Knowledge Ontology, which enables the explicit modeling of procedures and their executions, by reusing and extending existing ontologies. PKO is built on requirements collected from three heterogeneous industrial use cases and can be exploited by any AI and data-driven tools that rely on a shared and interoperable representation to support the governance of PK throughout its life cycle. We describe its structure and design methodology, and outline its relevance, quality, and impact by discussing applications leveraging PKO for PK elicitation and exploitation.

Problem

• Modeling tacit procedural knowledge in industries
• Enabling explicit representation of workflows and guidelines
• Supporting AI tools with interoperable knowledge ontology

Innovation

• PKO models procedures and executions explicitly
• PKO reuses and extends existing ontologies
• PKO supports AI and data-driven tools