Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies

TL;DR

This paper introduces verification mechanisms for ontology-based process models to ensure data consistency, unit compatibility, and completeness, facilitating reliable parameter interdependency modeling in manufacturing.

Contribution

It presents novel verification techniques including SPARQL filtering, unit consistency checks, and data completeness validation for ontology-based process models.

Findings

Effective data retrieval using SPARQL filtering

Successful unit consistency verification

Validated data completeness for model evaluation

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.