Ontology Enabled Hybrid Modeling and Simulation

TL;DR

This paper discusses how ontologies can improve hybrid modeling and simulation by enhancing semantic clarity, reusability, and interoperability, supported by practical case studies across various domains.

Contribution

It introduces a framework integrating methodological and referential ontologies with Semantic Web Technologies to address interoperability and formalization challenges in hybrid simulation.

Findings

Enhanced interoperability across systems and disciplines.

Successful application in sea-level rise and Industry 4.0 modeling.

Identification of challenges and future opportunities in ontology-based simulation.

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.