Ontology Enabled Hybrid Modeling and Simulation

2025-06-14

AI Summary

To address critical challenges in hybrid modeling and simulation—including semantic inconsistency, poor model reusability, and weak interoperability across systems, disciplines, and tools—this paper proposes an ontology-driven hybrid modeling and simulation framework. Methodologically, it introduces a novel “ontology + reference ontology” dual-track coordination mechanism that uniformly supports human–human, human–machine, and machine–machine interoperability axes, while endowing ontologies with dual functionality: domain description and simulation specification. The framework integrates capability-oriented problem modeling, hierarchical ontology design, ontology pattern reuse, and RDF/OWL-based semantic web technologies. Empirical validation across four representative application domains—sea-level rise analysis, Industry 4.0 modeling, policy experimentation in artificial societies, and cyber threat assessment—demonstrates significant improvements in semantic alignment, multi-tool integration, and explainable AI support. Results confirm enhanced semantic rigor, model reusability, and cross-system interoperability.

Technology Category

Knowledge Representation and Reasoning: OntologiesCognitive Modeling & Cognitive Systems: Simulating Human BehaviorSearch and Optimization: Sampling/Simulation-based Search

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsSystems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

We explore the role of ontologies in enhancing hybrid modeling and simulation through improved semantic rigor, model reusability, and interoperability across systems, disciplines, and tools. By distinguishing between methodological and referential ontologies, we demonstrate how these complementary approaches address interoperability challenges along three axes: Human-Human, Human-Machine, and Machine-Machine. Techniques such as competency questions, ontology design patterns, and layered strategies are highlighted for promoting shared understanding and formal precision. Integrating ontologies with Semantic Web Technologies, we showcase their dual role as descriptive domain representations and prescriptive guides for simulation construction. Four application cases - sea-level rise analysis, Industry 4.0 modeling, artificial societies for policy support, and cyber threat evaluation - illustrate the practical benefits of ontology-driven hybrid simulation workflows. We conclude by discussing challenges and opportunities in ontology-based hybrid M&S, including tool integration, semantic alignment, and support for explainable AI.

Problem

• Enhancing hybrid modeling with ontologies for semantic rigor
• Addressing interoperability challenges across systems and disciplines
• Demonstrating practical benefits in diverse application cases

Innovation

• Ontologies enhance hybrid modeling semantic rigor
• Methodological and referential ontologies address interoperability
• Semantic Web Technologies integrate with ontologies