Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies

• URL: http://arxiv.org/abs/2506.16087v1
• Date: Thu, 19 Jun 2025 07:21:16 GMT
• Title: Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies
• Authors: Tom Jeleniewski, Hamied Nabizada, Jonathan Reif, Felix Gehlhoff, Alexander Fay,
• Abstract summary: Formalization of process knowledge enables consistent modeling of parameter interdependencies in manufacturing.<n>To support cross-context application and knowledge reuse, such expressions are defined in a generic form and applied across multiple process contexts.<n>This paper presents a set of verification mechanisms for a previously developed process semantics-based model.
• Score: 40.539768677361735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Tool-Assisted Conformance Checking to Reference Process Models [0.9117519504551699]
  Conformity checks are crucial to maintain quality and consistency in various processes.<n>This paper explores automated conformance checks for concrete process models against reference models.
  arXiv  Detail & Related papers  (2025-08-01T16:08:24Z)
• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [55.914891182214475]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• Formal Analysis of the Contract Automata Runtime Environment with Uppaal: Modelling, Verification and Testing [0.3807314298073301]
  A distributed runtime application called tt CARE has been introduced to realise service applications specified using a dialect of finite-state automata.<n>We detail the formal modelling, verification and testing of tt CARE.
  arXiv  Detail & Related papers  (2025-01-22T15:03:25Z)
• From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping [0.0]
  This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) We utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding. The system adeptly handles data transformation and visualization, converting verbose extracted information into BPMN (Business Process Model and Notation) diagrams.
  arXiv  Detail & Related papers  (2023-12-16T12:35:28Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• Contextualized Machine Learning [40.415518395978204]
  Contextualized Machine Learning estimates heterogeneous functions by applying deep learning to the meta-relationship between contextual information and context-specific parametric models. We present the open-source PyTorch package ContextualizedML.
  arXiv  Detail & Related papers  (2023-10-17T15:23:00Z)
• 'What are you referring to?' Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchanges [65.03196674816772]
  Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities immediately and work with the speaker to repair it using meta-communicative, Clarification Exchanges (CE): a Clarification Request (CR) and a response. Here, we argue that the ability to generate and respond to CRs imposes specific constraints on the architecture and objective functions of multi-modal, visually grounded dialogue models.
  arXiv  Detail & Related papers  (2023-07-28T13:44:33Z)
• Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text [0.0]
  We present an extension to an existing pipeline to make it entirely data driven. We demonstrate the competitiveness of our improved pipeline, which not only eliminates the substantial overhead associated with feature engineering and rule definition. We propose an extension to the PET dataset that incorporates information about linguistic references and a corresponding method for resolving them.
  arXiv  Detail & Related papers  (2023-05-06T07:06:47Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Relational Action Bases: Formalization, Effective Safety Verification, and Invariants (Extended Version) [67.99023219822564]
  We introduce the general framework of relational action bases (RABs) RABs generalize existing models by lifting both restrictions. We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes.
  arXiv  Detail & Related papers  (2022-08-12T17:03:50Z)
• Conditional independence by typing [30.194205448457385]
  A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models. We show that for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships.
  arXiv  Detail & Related papers  (2020-10-22T17:27:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.