Procedural knowledge management in Industry 5.0: Challenges and opportunities for knowledge graphs

With digital transformation, industrial companies today are facing the challenges to change and innovate their business, by leveraging digital technologies and tools to support their processes and their operations. One of their main challenges is the management of the company knowledge, especially when tacit and owned by industry workers. In this paper, we illustrate how knowledge graphs can be the turning point to allow industry workers digitize and exploit the knowledge about the “what”, the “how” and the “why” of their everyday activities. In particular, we focus on the “how” by illustrating the challenges related to procedural knowledge management, i.e., the knowledge about processes and workflows that employees need to follow, and comply with, to correctly execute their tasks, in order to improve efficiency and effectiveness, to reduce risks and human errors and to optimize operations. We also explain the relationship in this context between knowledge graphs and sub-symbolic AI approaches. • Knowledge Graphs (KG) and AI improve Procedural Knowledge (PK) management in industry • PK ontologies help KG construction by supporting the collection of tacit knowledge • NLP techniques and LLMs help KG construction from existing procedure documentation • Procedural KGs empower Conversational AI to provide industry workers access to PK • Human-in-the-loop approaches ensure trust and acceptance of AI by industry workers