用本体提升混合建模与仿真的语义精度和跨系统兼容性
Ontology Enabled Hybrid Modeling and Simulation
- 区分方法论与指称本体，解决人-人、人-机、机-机三类互操作难题
- 结合语义网技术，使本体兼具领域描述与仿真构建指导功能
- 在海平面上升、工业4.0等四类应用中验证了流程可复用性与协同效率
本文探讨本体在提升混合建模与仿真中的作用，通过增强语义严谨性、模型复用性及跨系统、跨学科、跨工具的互操作性。通过区分方法论本体与指称本体，揭示其在人类间、人机间、机器间三类互操作挑战中的互补价值。提出使用能力问题、本体设计模式与分层策略，促进共享理解与形式化精确性。结合语义网技术，展示本体作为领域描述与仿真构建规范的双重角色。四个应用案例——海平面上升分析、工业4.0建模、政策支持的人工社会、网络威胁评估——证明本体驱动的混合仿真流程具有实践优势。最后讨论基于本体的混合建模与仿真面临的挑战与机遇，包括工具集成、语义对齐及可解释AI支持。
原文摘要 · Abstract (English)
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.
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