# Procedural Knowledge Ontology (PKO)

arXiv: https://arxiv.org/abs/2503.20634 · HTML: https://mlsanity.com/paper/2503.20634/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering](https://mlsanity.com/paper/2308.01597/) (2023)
- [The Landscape of Ontology Reuse Approaches](https://mlsanity.com/paper/2011.12599/) (2020)

## Built on by (most cited first, 2 of 2)

- [The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs](https://mlsanity.com/paper/2604.01728/) (2026)
- [Virtual Process Dossier: A Process-Aware Data Catalogue](https://mlsanity.com/paper/2607.27840/) (2026)

## Abstract (arXiv)

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.