Ontology-Driven Model-to-Model Transformation of Workflow Specifications

2025-11-17

AI Summary

Proprietary workflow languages (e.g., Smart Forms/Smart Flow) cause vendor lock-in, poor interoperability, and lack of knowledge traceability. To address these issues, this paper proposes an ontology-based, semantic-aware model transformation approach. It employs an RML-driven JSON→RDF/OWL semantic lifting pipeline, integrated with domain ontology alignment, logical reasoning, and declarative mapping rules to enable automated, verifiable M2M transformation from proprietary formats to BPMN 2.0. The key contribution lies in externalizing transformation knowledge as reusable ontologies and rules—supporting explicit control-flow representation, source-code-level traceability, and cross-vendor adaptability. Evaluated on 69 real-world workflows, the method generated 92 BPMN diagrams with a 94.2% success rate. User studies confirm significant improvements in process comprehension, diagnostic efficiency, and team collaboration.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Transforming proprietary workflow specifications to standard BPMN format
• Addressing vendor lock-in by enabling interoperability between workflow systems
• Preserving semantic traceability during model transformation through ontology alignment

Innovation

• Ontology-driven model transformation pipeline for workflows
• Semantic lifting and alignment to convert proprietary formats
• Generates standard BPMN with reusable mapping rules