Modeling State Causality in Energy Centred Cyber-Physical-Human Systems With OntoUML

Abstract

Energy-focused Cyber-Physical-Human Systems (CPHSs) depend on understanding how events, states, and temporal transitions shape system behaviour, particularly in settings where human actors, such as building occupants, grid operators, and domain experts, influence state evolution. However, relevant causal knowledge in such settings is often tacit, informally held by domain experts, and difficult to translate into formal conceptual models. Existing OntoUML-based methodologies provide semantic precision but offer limited structured guidance for eliciting such knowledge or for modelling evolving temporal states in domains with both physical and human-driven dynamics. To address this gap, we propose Tacit Knowledge Tree Onto (TKTOnto), a structured four-stage knowledge engineering workflow that integrates shared conceptualisation, object and event identification, and explicit temporal state modelling using established OntoUML constructs such as Phase and Situation. TKTOnto does not introduce new OntoUML primitives; rather, it provides systematic procedures for eliciting tacit, expert-based causal explanations through semi-structured interviews and translating them into OntoUML models grounded in Unified Foundational Ontology (UFO) semantics. The workflow is applied to two real-world energy- focused CPHS use cases, namely a smart building and a smart grid, and evaluated against four established OntoUML-based knowledge engineering methodologies. Results indicate that TKTOnto demonstrates stronger suitability for causal-temporal knowledge elicitation and modelling in the studied energy settings under the selected evaluation criteria. This work contributes a repeatable, methodology-driven approach to structuring causal knowledge in OntoUML for energy-focused CPHSs, supporting explainability and knowledge transfer in environments characterised by dynamic behaviour, human involvement, and expert- dependent knowledge.