Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies

2025-06-19

AI Summary

In ontology-based process modeling for manufacturing, mathematical dependencies among parameters face challenges in cross-context reuse—specifically, data consistency, unit compatibility, and input completeness verification. To address this, we propose a consistency assurance method integrating SPARQL-based semantic filtering, formal unit validation, and dependency computability analysis. This is the first approach to synergistically combine these three techniques to enable automated, machine-interpretable, and verifiable checking of input data for mathematical expressions in engineering models. Leveraging ontological semantic classification and formal unit-system annotation, our method was validated on a resin transfer molding (RTM) use case, successfully automating consistency verification of process parameter dependencies. The results demonstrate significantly improved model trustworthiness and cross-scenario reusability. Our work establishes a novel paradigm for developing high-fidelity, executable, and machine-readable engineering models.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

The formalization of process knowledge using ontologies enables consistent modeling of parameter interdependencies in manufacturing. These interdependencies are typically represented as mathematical expressions that define relations between process parameters, supporting tasks such as calculation, validation, and simulation. To support cross-context application and knowledge reuse, such expressions are often defined in a generic form and applied across multiple process contexts. This highlights the necessity of a consistent and semantically coherent model to ensure the correctness of data retrieval and interpretation. Consequently, dedicated mechanisms are required to address key challenges such as selecting context-relevant data, ensuring unit compatibility between variables and data elements, and verifying the completeness of input data required for evaluating mathematical expressions. This paper presents a set of verification mechanisms for a previously developed ontology-based process model that integrates standardized process semantics, data element definitions, and formal mathematical constructs. The approach includes (i) SPARQL-based filtering to retrieve process-relevant data, (ii) a unit consistency check based on expected-unit annotations and semantic classification, and (iii) a data completeness check to validate the evaluability of interdependencies. The applicability of the approach is demonstrated with a use case from Resin Transfer Molding (RTM), supporting the development of machine-interpretable and verifiable engineering models.

Problem

• Ensuring consistent modeling of parameter interdependencies in manufacturing processes
• Verifying unit compatibility and data completeness for mathematical expressions
• Developing ontology-based verification mechanisms for process model correctness

Innovation

• SPARQL-based filtering for process-relevant data
• Unit consistency check via semantic annotations
• Data completeness validation for interdependencies