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

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 primi-
tives; 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.