AI 中文总结
本文提出一种符合SAREF的本体，扩展SAREF4SYST本体加入AI相关概念，经智能电网场景验证，可实现边-雾-云环境下分布式AI工作流的语义互操作，部署成功率90%-100%，平均编排决策时间低于80ms。
AI 中文摘要
如今，语义模型在表示跨异构边缘、雾和云环境的分布式AI工作流及其执行方面支持有限。因此，AI流程和资源常采用不兼容的语义表示，影响互操作性、编排及复用。为应对这些挑战，本文提出一种符合SAREF的本体，用于表示跨边-雾-云连续体的分布式AI工作流。我们扩展SAREF4SYST本体，加入用于建模AI管道、可执行AI作业、计算资源、部署约束及通信关系的概念，提供AI工作流与异构计算基础设施的统一语义模型。该本体可实现分布式AI应用的语义互操作性、自动推理及资源感知编排，同时完全符合ETSI SAREF生态系统。通过概念验证智能电网能源服务编排场景对该本体进行评估，并通过能力问题验证其支持跨异构边-雾-云环境的AI工作流部署、执行推理及工作负载适配的能力。所有能力问题均通过SPARQL查询和语义推理成功验证。实验结果显示，在异构边-雾-云环境中，部署成功率达90%-100%，平均编排决策时间低于80ms，凸显其在保障分布式AI编排语义互操作性方面的有效性。
英文摘要
Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud environments. Therefore, AI processes and resources are often described using incompatible semantic representations, affecting the interoperability, orchestration, and reuse. To address these challenges, this paper proposes a SAREF-compliant ontology for representing distributed AI workflows across the edge-fog-cloud continuum. We extend the SAREF4SYST ontology with concepts for modeling AI pipelines, executable AI jobs, computational resources, deployment constraints, and communication relationships, providing a unified semantic model of both AI workflows and heterogeneous computing infrastructures. The ontology enables semantic interoperability, automated reasoning, and resource-aware orchestration of distributed AI applications while remaining fully aligned with the ETSI SAREF ecosystem. The ontology is evaluated using proof-of-concept smart grid energy services orchestration scenarios and validated using competency questions showing its ability to support AI workflow deployment, execution reasoning, and workload adaptation across heterogeneous edge, fog, and cloud environments. All competency questions were successfully validated using SPARQL querying and semantic reasoning. Experimental results demonstrate deployment success rates of 90-100% with average orchestration decision times below 80 ms across heterogeneous edge-fog-cloud environments, highlighting its effectiveness on ensuring semantic interoperability for distributed AI orchestration.
