Ontology on Pi.Kappa_

An Ontology Framework for Human-Robot Interoperability in Dynamic Construction Environments

Mon, 01 Jan 0001 00:00:00 +0000

Karapanagiotis, P., Koester, F., & Emmanouilidis, C. (2025). An Ontology Framework for Human-Robot Interoperability in Dynamic Construction Environments. In M. Dassisti, K. Madani, & H. Panetto (Editors), Innovative Intelligent Industrial Production and Logistics (pp. 313–331). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80775-6_22

Humans and robots increasingly work collaboratively in work environments. Their synergy varies between simple co-existence, wherein they perform independently in shared spaces, to deep collaboration, wherein the joint outcome of their co-working cannot be attributed to either one of the two types of actors alone. Relevant concepts are that of human agency, when robots operate under human oversight, with humans assuming control in appropriate circumstances, and sliding autonomy, where control slides back and forth between humans and robots, depending on the situational context. This study introduces an ontology framework for human-robot interoperability (HRI) in dynamic construction environments. The ontology serves as a context model, facilitating task allocation and collaboration between humans and robots under a Construction Sliding Work Sharing (CSWS) scheme. While previous ontology designs focused either on aspects of construction processes or human-robot collaboration, the ontological formalization of the intersection of HRI and dynamic construction environments is unexplored. The CSWS ontology integrates human-centricity, dynamic task distribution, and human-robot interoperability concepts in construction settings. The ontology development process combines concepts distilled from qualitative content analysis of a construction case study with relevant concepts from previous literature and ontologies. The ontology is consistent with the Sliding Work Sharing (SWS) concept, an extension of sliding autonomy for dynamic task allocation across human and robot actors. The capacity of the CSWS ontology to support dynamic task allocation and human-robot collaboration is exemplified via use cases querying for agent safety, process errors, and human-robot collaborative activities.

Enabling interoperable human–AI teaming for automation in construction and manufacturing via Digital Twins and Sliding Work Sharing ontologies

Mon, 01 Jan 0001 00:00:00 +0000

Karapanagiotis, P., Kottagaha, K. W. M., Rovere, D., Bokhorst, J. A. C., Valdata, A., & Emmanouilidis, C. (2025). Enabling interoperable human–AI teaming for automation in construction and manufacturing via Digital Twins and Sliding Work Sharing ontologies. In Journal of Industrial Information Integration (Vol. 48). https://doi.org/10.1016/j.jii.2025.100962

This paper introduces an ontology system to support dynamic, explainable, and human-centric collaboration between humans and artificial intelligence-enabled non-human agents in cyber–physical environments. In this setting, Digital Twins (digital models of 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.

A Unified Ontology Framework for Human-Centered Digital Twins

Mon, 01 Jan 0001 00:00:00 +0000

Kottagaha, K. W. M., Karapanagiotis, P., Bokhorst, J. A. C., & Emmanouilidis, C. (2026). A Unified Ontology Framework for Human-Centered Digital Twins. In J. Barata, K. Madani, & H. Panetto (Editors), Innovative Intelligent Industrial Production and Logistics (pp. 135–151). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-15579-5_9

This paper presents a top-level ontology framework for Human-Centered Digital Twins designed to enhance semantic interoperability and reasoning in socio-technical systems. Existing ontologies are typically domain-specific, which eases coverage of a specific domain but limits cross-domain reuse and adaptability. Addressing this gap, the proposed ontology is structured around three foundational concepts: Digital Twins, Human-Centricity, and Socio-technical System Actors within the system. The ontology was developed following established ontology engineering practices, with emphasis on modularity, reusability, and formal representation of key concepts. It supports the integration of sensor observations, interaction modalities, and representation of human-centricity factors. Its applicability is demonstrated through a logistics use case, which involves cognitive decision support for yard operators and forklift drivers engaged in short-term scheduling of yard operations. The ontology offers a reusable semantic foundation that supports integration, context representation, and human-centric system modelling and can be extended to diverse domains.