Proprietary workflow modeling languages such as Smart Forms & Smart Flow hamper interoperability and reuse because they lock process knowledge into closed formats. To address this vendor lock-in and ease migration to open standards, we introduce an ontology-driven model-to-model pipeline that systematically translates domain-specific workflow definitions to Business Process Model and Notation (BPMN) 2.0. The pipeline comprises three phases: RML-based semantic lifting of JSON to RDF/OWL, ontology alignment and reasoning, and BPMN generation via the Camunda Model API. By externalizing mapping knowledge into ontologies and declarative rules rather than code, the approach supports reusability across vendor-specific formats and preserves semantic traceability between source definitions and target BPMN models. We instantiated the pipeline for Instituto Superior Técnico (IST)'s Smart Forms & Smart Flow and implemented a converter that produces standard-compliant BPMN diagrams. Evaluation on a corpus of 69 real-world workflows produced 92 BPMN diagrams with a 94.2% success rate. Failures (5.81%) stemmed from dynamic behaviors and time-based transitions not explicit in the static JSON. Interviews with support and development teams indicated that the resulting diagrams provide a top-down view that improves comprehension, diagnosis and onboarding by exposing implicit control flow and linking tasks and forms back to their sources. The pipeline is generalizable to other proprietary workflow languages by adapting the ontology and mappings, enabling interoperability and reducing vendor dependency while supporting continuous integration and long-term maintainability. The presented case study demonstrates that ontology-driven M2M transformation can systematically bridge domain-specific workflows and standard notations, offering quantifiable performance and qualitative benefits for stakeholders.
翻译:专有工作流建模语言(如Smart Forms & Smart Flow)由于将流程知识锁定在封闭格式中,阻碍了互操作性和重用性。为解决这种供应商锁定问题并促进向开放标准的迁移,我们提出了一种本体驱动的模型到模型转换流水线,系统地将领域特定工作流定义转换为业务流程模型与标注(BPMN)2.0。该流水线包含三个阶段:基于RML的JSON语义提升至RDF/OWL、本体对齐与推理,以及通过Camunda Model API生成BPMN。通过将映射知识外部化为本体和声明式规则而非代码,该方法支持跨供应商特定格式的重用,并保持源定义与目标BPMN模型之间的语义可追溯性。我们针对里斯本高等理工学院(IST)的Smart Forms & Smart Flow实例化了该流水线,并实现了一个生成符合标准的BPMN图的转换器。在包含69个真实工作流的语料库上评估,生成了92个BPMN图,成功率达94.2%。失败案例(5.81%)源于静态JSON中未明确表达的动态行为和时间驱动转换。对支持和开发团队的访谈表明,生成的图表提供了自上而下的视图,通过揭示隐式控制流并将任务和表单链接回其来源,改善了理解、诊断和人员培训效果。该流水线可通过调整本体和映射推广至其他专有工作流语言,在支持持续集成和长期可维护性的同时,实现互操作性并降低供应商依赖性。本案例研究表明,本体驱动的M2M转换能系统性地桥接领域特定工作流与标准标注,为利益相关者提供可量化的性能和定性收益。