Ontology-based Requirements Transformation

• URL: http://arxiv.org/abs/2608.21945v1
• Date: Sat, 22 Aug 2026 13:08:45 GMT
• Title: Ontology-based Requirements Transformation
• Abstract summary: This paper presents an ontology-based approach to the supply chain-aware transformation of functional and environmental load requirements given by so-called Mission Profiles (MPs)<n>The approach aims at improving the efficiency of the engineering process through supporting the transformation process and enabling a better integration of the transformation into existing Model-based Systems Engineering (MBSE) processes.
• Score: 39.82496881457754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an ontology-based approach to the supply chain-aware transformation of functional and environmental load requirements given by so-called Mission Profiles (MPs). The approach aims at improving the efficiency of the engineering process through supporting the transformation process and enabling a better integration of the transformation into existing Model-based Systems Engineering (MBSE) processes. We propose a methodology and a supporting system which aids in the transformation process while the latter feature is obtained by constructing and working on models. Consequent utilization of the standardized language OWL to express model representations further enables better knowledge integration and transfer among hetero-geneous systems. In addition to that, this favors knowledge reuse across projects which can reduce overall costs. Moreover, the system enables stripping off irrelevant information from MPs, thus improving protection of intellectual property.

Related papers

• AI Assisted Workflow Optimization and Automation [3.2066047088217347]
  This study focuses on the process characteristics of auxiliary compliance work.<n>It proposes an optimization path with process reengineering, system modeling, and technology integration as the core.<n>Research suggests that the systematic optimization and intelligent upgrading of auxiliary processes will help build a modern compliance operation system.
  arXiv  Detail & Related papers  (2026-09-13T06:41:55Z)
• Ontology-supported Design Parameter Management for Change Impact Analysis [65.57339762906196]
  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.
  arXiv  Detail & Related papers  (2026-08-22T13:25:31Z)
• Development of Ontological Knowledge Bases by Leveraging Large Language Models [1.1470070927586018]
  Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems.<n>Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development.<n>This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles.
  arXiv  Detail & Related papers  (2026-01-15T14:30:42Z)
• PISA: A Pragmatic Psych-Inspired Unified Memory System for Enhanced AI Agency [50.712873697511206]
  Existing work often lacks adaptability to diverse tasks and overlooks the constructive and task-oriented role of AI agent memory.<n>We propose PISA, a pragmatic, psych-inspired unified memory system that treats memory as a constructive and adaptive process.<n>Our empirical evaluation, conducted on the existing LOCOMO benchmark and our newly proposed AggQA benchmark for data analysis tasks, confirms that PISA sets a new state-of-the-art by significantly enhancing adaptability and long-term knowledge retention.
  arXiv  Detail & Related papers  (2025-10-12T10:34:35Z)
• Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems [2.9297240479517836]
  We propose a unified three-phase framework that integrates process-based models with machine learning (ML)<n>Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification.<n>Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN)<n>Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents.
  arXiv  Detail & Related papers  (2025-09-02T05:24:35Z)
• In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory [54.92893355284945]
  Deep learning-based wireless receivers offer the potential to dynamically adapt to varying channel environments.<n>Current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate explicit optimization through gradient descent.<n>This paper presents gradient-free adaptation techniques rooted in the emerging paradigm of in-context learning (ICL)
  arXiv  Detail & Related papers  (2025-06-18T06:43:55Z)
• Generative transformations and patterns in LLM-native approaches for software verification and falsification [1.4595796095047369]
  We argue that a foundational step towards a more disciplined engineering practice is a systematic understanding of the core functional units-generative transformations.<n>We first present a fine-grained taxonomy of generative transformations, abstracting prompt-based interactions into conceptual signatures.<n>Our analysis not only validates the utility of the taxonomy but also surfaces strategic gaps and cross-dimensional relationships.
  arXiv  Detail & Related papers  (2024-04-14T23:45:23Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• 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)

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.