Procedural Knowledge Ontology (PKO)

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.

Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models

Procedural Knowledge is the know-how expressed in the form of sequences of steps needed to perform some tasks. Procedures are usually described by means of natural language texts, such as recipes or maintenance manuals, possibly spread across different documents and systems, and their interpretation and subsequent execution is often left to the reader. Representing such procedures in a Knowledge Graph (KG) can be the basis to build digital tools to support those users who need to apply or execute them. In this paper, we leverage Large Language Model (LLM) capabilities and propose a prompt engineering approach to extract steps, actions, objects, equipment and temporal information from a textual procedure, in order to populate a Procedural KG according to a pre-defined ontology. We evaluate the KG extraction results by means of a user study, in order to qualitatively and quantitatively assess the perceived quality and usefulness of the LLM-extracted procedural knowledge. We show that LLMs can produce outputs of acceptable quality and we assess the subjective perception of AI by human evaluators.