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

2025-12-10

AI Summary

To address challenges in Cyber-Physical Systems (CPS) development—including heterogeneous formal models, fragmented storage of modeling artifacts, inadequate version management, and limited knowledge reuse—this paper proposes an ontology-driven engineering knowledge graph framework. It introduces a unified systems engineering ontology built upon the custom Ontology Modelling Language (OML), enabling semantic integration of modeling artifacts across formal methods (e.g., SysML, UML, Modelica). The framework integrates a workflow engine, SPARQL querying, SWRL rule-based reasoning, and versioned graph storage to implicitly encapsulate complex knowledge graph operations. It is the first to support full-lifecycle semantic interoperability and automated knowledge discovery. Evaluated on an electric-drive intelligent sensor system, the framework significantly improves model version management efficiency, accelerates information retrieval, and uncovers three categories of latent engineering knowledge via inference.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and 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
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Managing modeling artifacts from complex engineering workflows
• Storing and reasoning on workflow data using ontology-based knowledge graphs
• Reducing information access time and inferring new knowledge from stored data

Innovation

• Ontology-based knowledge graph for system engineering workflows
• Tools for workflow design, enactment, and artifact storage
• Framework applied to real-world drivetrain smart sensor system