Process Knowledge Management, Part IV

2025-07-29 · Source: Intentional Arrangement · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, long

Summary

The Procedural Knowledge Ontology (PKO), developed by Cefriel in collaboration with industrial partners like Beko Europe, Fagor Automation, and Siemens, provides a formal framework for explicitly modeling and representing industrial procedures, their executions, and related resources. This ontology addresses the challenge of capturing both tacit and explicit process knowledge in industrial settings, where much critical information resides in experienced operators' minds or fragmented documentation. PKO distinguishes between abstract procedure specifications and concrete execution instances, using a modular design with a core module and industry-specific extensions. Its implementation at Beko Europe for Lockout/Tagout (LOTO) safety procedures demonstrated how systematic knowledge elicitation and semantic encoding can transform tacit understanding into explicit, computationally accessible data, enabling tools like web-based elicitation interfaces and knowledge graph-empowered chatbots for operational support.

Key takeaway

For AI Engineers and MLOps Engineers building industrial AI systems, integrating a procedural knowledge infrastructure like PKO is critical. You should invest in systematic knowledge elicitation from domain experts and encode this knowledge using formal ontological structures. This approach, exemplified by PKO's use of knowledge graph-empowered RAG, ensures AI systems can access accurate, contextually rich procedural understanding, leading to improved compliance, reduced errors, and more effective AI deployments.

Key insights

Formal ontologies like PKO are crucial for transforming tacit procedural knowledge into structured, AI-accessible data.

Principles

• Distinguish abstract procedures from concrete executions.
• Reuse existing semantic standards for interoperability.
• Prioritize systematic elicitation of tacit knowledge.

Method

PKO development follows the Linked Open Terms (LOT) methodology, an iterative, reuse-based approach involving requirements specification, implementation, publication, and maintenance, with a strong focus on industrial application and domain expert engagement.

In practice

• Implement web tools for expert-guided procedure documentation.
• Deploy chatbots for real-time procedural guidance via knowledge graphs.
• Utilize GraphRAG architectures to ground AI responses in formal knowledge.

Topics

• Procedural Knowledge Ontology
• Process Knowledge Management
• Knowledge Graphs
• Industrial AI
• Retrieval-Augmented Generation

Code references

• perks-project/pk-ontology

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Intentional Arrangement.