Jul 2026· Proceedings of the INCOSE AOSEC 2025· 0 citations
TL;DR
This paper argues that ontology-driven approaches provide the foundation for an acquisition ecosystem that is not only more coherent and collaborative but also adaptive to the dynamic conditions of the 21st century.
Abstract
Systems engineering has historically provided an effective framework for managing large-scale acquisitions. Yet, as systems grow in size, complexity, and timeframe, traditional approaches are becoming less effective. Accelerating rates of change and high levels of uncertainty reduce the long-term value of acquired systems, while the scale of modern programs increases costs, coordination demands, and timelines — heightening the risk of obsolescence before delivery. These pressures raise fundamental questions about the continued viability of conventional acquisition practices. In response, tools such as Model-Based Systems Engineering (MBSE), Agile Systems Engineering (AgileSE), and digital engineering have been introduced. While these provide incremental performance gains, they do not resolve the core challenge: how to manage acquisition in environments where predictability and uncertainty coexist. Doing the same things faster is not enough; a shift in mindset and method is required. This paper argues that ontology-driven approaches provide the foundation for that shift. Ontologies — machine-readable representations of concepts, relationships, and constraints — create a semantic backbone that integrates models, data, and stakeholder perspectives. By embedding ontologies into acquisition systems, organizations can simultaneously bridge predictive and adaptive paradigms, enhance traceability and interoperability across technical, financial, and operational domains, and enable automated reasoning to expose dependencies, conflicts, and opportunities. The result is an acquisition ecosystem that is not only more coherent and collaborative but also adaptive to the dynamic conditions of the 21st century. Many of the elements needed for this transformation already exist; the critical step forward is adopting ontologies as the integrative layer that unites them.
Traditional enterprise resource planning (ERP) systems struggle to adapt to rapidly evolving legal environments because of their static architectures and dependence on manual updates, particularly in developing economies. This study addresses this limitation by proposing a context-aware, artificial intelligence (AI)-driven framework that enables ERP systems to interpret and adapt to evolving legal requirements. The framework integrates ontology-based reasoning, natural language processing (NLP), and adaptive learning to transform legislative changes into machine-interpretable rules and executable process updates, supported by human validation to ensure accuracy and accountability. Designed using a design-oriented research approach, the framework establishes a structured architecture that supports continuous, traceable, and adaptive compliance. The findings demonstrate the framework’s potential to enhance regulatory alignment, improve transparency, and reduce dependence on manual system updates. The study contributes to the development of intelligent and explainable ERP systems capable of sustaining real-time compliance in dynamic regulatory environments.
Shkëlqim Miftari, A. Aliu, A. Luma· International Journal of Int...· 1 citation
This paper builds on the author’s previous work regarding domain-specific ontologies (DSO) and its importance in the human-factors integration (HFI) space. Explicit term definitions captured by a DSO allow the HFI vocabulary to be mapped into a model-based enterprise architecture (MBEA). Integrating this terminology into the overall MBEA provides insight into the role that individuals play by considering personnel as a critical system component. Often considered external actors, human resources are typically not accounted for in the original solution design. However, MBEA promises to reverse this trend by implementing the Unified Architecture Framework (UAF). The UAF is composed of various domains and their aspects and is meant to graphically illustrate enterprise concepts such as strategy, operations, resources, personnel, and services in a digital environment. Capturing HFI information in a model improves the traceability of person(s) and organizational concerns, responsibilities, and competencies to highlight gaps that must be addressed. The incorporation of the HFI DSO into an MBEA enhances communication between disciplines and provides transparency for stakeholders. This research demonstrates the feasibility of constructing a DSO based on an HFI body of knowledge; leveraging the Web Ontology Language (OWL), the subject-predicate-object (SPO) approach, and the Protégé ontology editor. It also shows that by importing the OWL file into a concept model, understanding HFI terms facilitates MBEA while maintaining personnel as a critical part of a successful organization. This research identifies areas for improvement of the UAF domain-specific modeling language (DSML) to ensure that it adequately addresses HFI concerns by mapping like-terms.
Digitalization and regulatory compliance pose substantial challenges to companies, requiring adjustments to operations and business processes. Smooth transitions can be facilitated by analyzing discrepancies between current and target processes, enabling the identification of necessary organizational changes. Based on these insights, change managers can develop action plans to support effective implementation and ensure return on investment. Although scholars emphasize the importance of data-driven evaluation in change management (CM) and recognize the value of information embedded in business process models, the literature lacks systematic methods for extracting and integrating such information, particularly from text-based sources. In collaboration with industrial partners, we developed a method to address this gap. Our approach integrates semantic business process management, text analytics, and CM to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations. The method also resolves terminological inconsistencies across heterogeneous sources. This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers. To demonstrate applicability, we implemented a proof of concept in an industrial environment to validate process adherence against natural language documents such as industry standards
Domonkos Gáspár, Ildikó Szabó, Katalin Ternai et al.· Journal of Industrial Integr...· 0 citations
Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of LLM outputs. This paper introduces OntoExtend, a requirements-driven framework for ontology extension with LLMs. It uses retrieval-augmented generation (RAG) over relevant input ontologies and requirements in the form of competency questions to propose grounded extensions. We evaluate OntoExtend on 39 CQs from two use cases: a public EU-project ontology, Onto-DESIDE, and an industrial ontology from Bosch. The generated fragments show few structural issues, satisfy all functional evaluation tests, and are rated by ontology engineers as requiring minor to moderate revision before integration. These results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real world scenarios, while remaining sensitive to CQ specificity and modelling profile.
Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid et al.· 0 citations
Rapid advances in Information Technology (IT) and Artificial Intelligence (AI) have resulted in increasingly complex socio‑technical systems, placing new demands on human–automation collaboration. These demands are particularly acute in VUCA (Volatile, Uncertain, Complex, Ambiguous) environments, where failures can have immediate and severe consequences. Effective human‑automation teams must therefore adapt task and control allocation dynamically, while maintaining safety, accountability, and operator understanding. Adaptive automation (AA) has been widely studied as a means to support such flexibility, often described using Levels of Automation (LoA) frameworks. However, many existing LoA frameworks do not align well with decision‑cycle models commonly used to reason about responsibility distribution in VUCA contexts. This misalignment complicates the design and reuse of AA-solutions for real‑world applications. Team Design Patterns (TDPs) offer a promising approach by capturing reusable solutions to recurring challenges in human–automation teamwork. Yet, the systematic construction of coherent TDP sets remains difficult due to a lack of structured design support. To address this, we present an integrated human‑automation teaming framework that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers. The framework extends NASA’s eight‑level LoA framework by assigning descriptive level names, explicit human‑loop status (in/on/out‑of‑the‑loop), and visual responsibility mapping inspired by the European Defence Agency’s methods‑of‑control framework. Based on a naval Uncrewed Surface Vessel use case, we developed and evaluated three TDPs with domain experts: uniform task delegation, uniform goal delegation, and a mixed‑level pattern combining different LoA across decision-cycle stages. Expert evaluation confirmed that mixed‑level TDPs best reflect operational, regulatory, and technological realities. Overall, the framework provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Jelle A Van Dijk, Rosa van Tuijn, Renske Verwaal-Bootsma et al.· AHFE International· 0 citations
AI-assisted software development approaches, such as vibe coding, enable rapid code generation but lack the governance and reliability required for sustaining engineering in enterprise software. In these environments, traceability, security, technical debt management, and architectural integrity are critical for any software modification. This paper presents ATeam, a framework that facilitates AI-assisted software development through a structured and auditable maintenance process incorporating human oversight. The framework employs a multi-phase pipeline that enforces impact analysis and explicit approval gates. ATeam is evaluated on 24 sustaining engineering tasks spanning four IEEE maintenance categories, utilizing three distinct large language models (LLMs). A set of interdependent microservices is developed to assess the system. ATeam achieves an 82.5 end-to-end score. The results demonstrate that structured decomposition and governance reduce dependence on model scale, with smaller models remaining competitive with larger ones. This finding enables regulated industries to leverage AI-assisted development using on-premises models. Comparative evaluation against AutoGPT-style and unconstrained baselines reveals that ATeam achieves statistically significant improvements (Welch's $p<10^{-6})$ with large effect sizes. The evidence suggests that governance, rather than agentic execution alone, is the primary determinant of reliable enterprise software sustaining engineering.
Salvatore Vella, Alex Ferworn, Malek Sharieh· 2026 6th International Confe...· 0 citations