Industrial Ontology Foundry (IOF) Core Ontology (IOFCORE)

physical systems or processes that mirror their real-time state) and Human Digital Twins (digital representations of individual humans, including their physiological or cognitive states) may provide information to enable an appropriate dynamic allocation of the work that can be shared by humans and AI actors (i.e., sliding work sharing). A novel upper-level Sliding Work Sharing ontology is defined to support semantic interoperability and reasoning across diverse domains, facilitating sliding work sharing in complex environments. The ontology is grounded in Industry 5.0 concepts and built upon the Industrial Ontology Foundry core ontology. It extends conventional scheduling ontologies by incorporating key constructs for Digital Twins, Human Digital Twins, and dynamic task flows. We validate the ontology through two use cases from the domains of automation in construction and manufacturing. The collaborative construction case involves robots and humans, while the manufacturing one integrates legacy systems, artificial intelligence actors, and human planners. The developed ontology system is evaluated for its coverage and expressiveness through a novel Retrieval-Augmented Generation based methodology, applied on diverse Large Language Models to derive competency questions from external sources. This approach enhances conventional ontology validation techniques with a scalable and unbiased alternative. Logical consistency is confirmed using a range of standard reasoners. Our results demonstrate that the Sliding Work Sharing ontology has considerable flexibility and potential to advance human–AI teaming in future work environments. • Digital Twins and Sliding Work Sharing ontologies. • Large language model retrieval augmented generation for ontology evaluation. • Human AI-teaming in construction and manufacturing automation.

for demonstrating and evaluating the digitalization process, the primary datasets from low‐cycle‐fatigue testing of several copper alloys are prepared. The fatigue test ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the basic formal ontology, industrial ontology foundry core ontology, and material science and engineering ontology. The ckan.kupferdigital data management system is also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph and automatically convert all the primary datasets to those machine‐readable data which are represented by the web ontology language. The retrievability of the converted digital data is also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web.

control, with many controlled and non-controllable variables that impact the quality of bioproducts. Representing biomanufacturing process knowledge, control models, and actual occurrences in coherent ontologies could aid both humans and computers in dealing with the complexity. However, there is a lack of such coherent ontologies. Even though the Industrial Ontology Foundry (IOF) Core ontology has provided a groundwork based on the widely used Basic Formal Ontology (BFO) for such ontological requirements, there are still insufficient constructs and clear guidance on the representation of digital artifacts and their correspondences to the physical counterparts. This paper presents a framework to extend the IOF Core to address the gap. The framework is founded on establishing a counterpart (CR) relation pattern presented in our previous paper. Counterpart relation was selected for its ability to facilitate a more intuitive and concise representation of many kinds of digital artifacts (e.g., planned, designed) and physical entities (e.g., planning process, manufacturing process). We validated the approach with a process verification of a fed-batch bioreactor operation. The paper started by defining the use case requirement, which was followed by an ontology development. A knowledge graph of the bioprocess plan and occurrences of processes in the plan was then instantiated. Competency questions were used to concretize the ontology requirement from the use case, and subsequently, an executable set of queries was created from them and was used to computationally validate the ontology against the requirement. The GraphDB tool was used to support the validation. The result of this research not only showed that the CR pattern described in our previous paper could satisfy the requirements related to the digital thread of digital and physical process information, but it also demonstrated that several visualization approaches on graph data can be used to address competency questions. These findings provide insights into the future of data integration and management within biomanufacturing, highlighting the role of ontologies for improved data interoperability and analysis.