Model management to support systems engineering workflows using ontology-based knowledge graphs

TL;DR

The paper presents an ontology-based knowledge-graph framework for model management in system engineering, addressing fragmentation across formalisms and tools by leveraging OML, RDF/OWL, and SPARQL. It introduces a full toolchain (Adaptor Service, WEE, Draw.io frontend, and a KG backbone in Fuseki) to design, enact, and reason about workflows and artefacts, with fine-grained traceability and versioning capabilities. A running mass-spring-damper example and a real drivetrain case study demonstrate how explicit workflow modelling, artefact typing, and queryable provenance reduce effort and improve information access. The work situates itself among MBSE and digital-twin efforts, highlighting practical gains in repeatability, data governance, and knowledge reuse while acknowledging current limitations in language evolution and scalable versioning. Overall, the framework provides a flexible, extensible path toward cognition-enabled knowledge management in complex engineering lifecycles.

Abstract

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber- Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.

Figures (21)

• Figure 1: Example of OML specification.
• Figure 2: Simple representation of the system.
• Figure 3: Causal Block Diagram model of the mass-spring-damper system.
• Figure 4: System trace of a single mass-spring-damper causal block diagram simulation.
• Figure 5: Running Example: Mass-spring-damper Workflow Model.