Ontology-supported Design Parameter Management for Change Impact Analysis

• URL: http://arxiv.org/abs/2608.21949v1
• Date: Sat, 22 Aug 2026 13:25:31 GMT
• Title: Ontology-supported Design Parameter Management for Change Impact Analysis
• Abstract summary: OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems.<n>The approach is suitable for both software and hardware designs.
• Score: 65.57339762906196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an ontology-supported approach to the management of design parameters in engineering. This approach aims specifically at enabling Change Impact Analysis through Requirements Traceability and acquainted expert knowledge of design parameters. The approach is suitable for both software and hardware designs. The activities and features are mainly obtained by (1) the application of an ontology-based universal system modeling procedure proposal for model integration, (2) the utilization of a knowledge base for capturing expert knowledge and (3) a semantic Mission Profile Aware Design platform. OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems which are common in complex engineering projects. At the same time, effort to perform reasoning on such models can be reduced. A demonstration and hands-on description of two illustrative use cases complements the paper.

Related papers

• Semi-Automated Design of Data-Intensive Architectures [49.1574468325115]
  This paper introduces a development methodology for data-intensive architectures.<n>It guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application.<n>We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners.
  arXiv  Detail & Related papers  (2025-03-21T16:01:11Z)
• A Survey of Model Architectures in Information Retrieval [59.61734783818073]
  The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP)<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude with a forward-looking discussion of emerging challenges and future directions.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems.<n>We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models [9.900586490845694]
  This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. We demonstrate our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and TabCSDI in both the accuracy and diversity of imputation options. The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems.
  arXiv  Detail & Related papers  (2024-06-17T16:03:17Z)
• MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities [72.05167902805405]
  We present MergeNet, which learns to bridge the gap of parameter spaces of heterogeneous models.<n>The core mechanism of MergeNet lies in the parameter adapter, which operates by querying the source model's low-rank parameters.<n> MergeNet is learned alongside both models, allowing our framework to dynamically transfer and adapt knowledge relevant to the current stage.
  arXiv  Detail & Related papers  (2024-04-20T08:34:39Z)
• Beyond development: Challenges in deploying machine learning models for structural engineering applications [2.6415688445750383]
  This paper aims to illustrate the challenges of developing machine learning models suitable for deployment through two illustrative examples. Among various pitfalls, the presented discussion focuses on model overfitting and underspecification, training data representativeness, variable omission bias, and cross-validation. Results highlight the importance of implementing rigorous model validation techniques through adaptive sampling, careful physics-informed feature selection, and considerations of both model complexity and generalizability.
  arXiv  Detail & Related papers  (2024-04-18T23:40:42Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Methodology for Holistic Reference Modeling in Systems Engineering [0.0]
  This paper presents a holistic approach to describe reference models across different views and levels. Benefits include an end-to-end traceability of the capability coverage with performance parameters considered already at the starting point of the reference design.
  arXiv  Detail & Related papers  (2022-11-21T13:41:07Z)
• Explainable AI for Engineering Design: A Unified Approach of Systems Engineering and Component- Based Deep Learning Demonstrated by Energy- Efficient Building Design [0.8265473560266577]
  We propose a component-based approach to create, partial component models by machine learning (ML) This, component-based approach aligns deep learning with systems, engineering (SE) The, approach adapts the model structure to engineering methods of, systems engineering and to domain knowledge.
  arXiv  Detail & Related papers  (2021-08-30T07:20:58Z)

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.