This paper describes the step-by-step processes towards the formalization of a core ontology for missions and capabilities in systems of systems, and the development of a specific system of systems domain ontology from the formalized ontology. The study traces the ontology development process through the SABIOx methodology’s requirements, setup, capture, design, and implementation phases. In this process, we demonstrate the core ontology’s usability and reusability. Usability refers to the ontology’s adequacy for specific use as a reference point for SoS knowledge exploration for development and operational purposes, and reusability refers to the ontology’s adequacy for several uses, such as facilitating the understanding of different domain-specific systems of systems. This demonstration is done in three steps: formalization of the core ontology, exploration of the usefulness of this formalization, and development of a domain ontology from the core ontology. These result in: the incorporation of systematic ontology development processes; the application of ontology tools for machine readability; coherence and consistency checking of the ontology artifact; querying support for the ontology knowledge base; and testing of the core knowledge with a domain-specific system of systems. An alignment of these aspects provides different points of view of how a system of systems can be formulated, how the concepts collectively describe the development of an SoS emergent behavior, and how the ontology knowledge base can be explored to support decision frameworks guiding a system of systems.

A core ontology of organizational policies

Road technical condition assessment and maintenance decision-making rely heavily on technical standards whose clauses, computational formulas, and decision logic are often expressed in unstructured formats, leading to fragmented knowledge representation, isolated indicator calculation procedures, and limited interpretability of decision outcomes. To address these challenges, a semantic framework with executable reasoning and computation components, Road Performance and Maintenance Ontology (RPMO), was developed, composed of a core ontology, an assessment ontology, and a maintenance ontology. The framework formalized clauses, computational formulas, and decision rules from standards and integrated semantic web rule language (SWRL) rules with external computational programs to automate distress identification and the computation and write-back of performance indicators. Validation through three use case scenarios conducted on eleven expressway asphalt pavement segments demonstrated that the framework produced distress severity inference, indicator computation, performance rating, and maintenance recommendations that were highly consistent with technical standards and expert judgment, with all reasoning results traceable to specific clauses and rule instances. This research established a methodological foundation for semantic transformation of road technical standards and automated execution of assessment and decision logic, enhancing the efficiency, transparency, and consistency of maintenance decision-making to support explicit, reliable, and knowledge-driven intelligent systems.

Enabling digital and machine-readable records for products aggregating information about their entire lifecycle is the underlying idea of digital product passports (DPP). June 2024 marked an important milestone for DPPs as the European Parliament and the Council adopted the Ecodesign for Sustainable Products Regulation (ESPR), outlining also the requirements for DPPs and the sustainability-related product parameters that these may need to include. In the long term, DPPs may be applicable to nearly all physical goods placed on the EU market or put into service. Establishing a DPP ecosystem will be a complex task with many intricate system parts to be integrated. One of the challenges for DPP ecosystems is how to capture and reflect the knowledge and information about products in an unambiguous way, and enable semantic interoperability with already existing and to-be built DPP systems. A feasible approach is to use ontologies. In this article, we establish requirements for the development of an upper-level ESPR-compliant core ontology, and analyse the key notions for such a core ontology solely relying on the ESPR. These requirements serve as a basis for the design and development of an ESPR-compliant DPP core ontology but could be extended beyond the ESPR and DPP scope.

The European Union’s Digital Product Passport (DPP) is a €1.8 trillion initiative to enable the circular economy, but its success depends on solving a fundamental paradox. The very ontologies designed to ensure interoperability are at risk of creating new ”conceptual silos” because they lack a logically sound foundation, consequently risking the initiative. This paper argues that the solution lies in grounding DPP ontologies in a robust upper ontology like Basic Formal Ontology (BFO). Through a systematic analysis of the CIRPASS-2 Core Ontology, we reveal critical ambiguities—notably the conflation of physical processes with digital instructions— that make the current model inherently brittle. We then present a refined BFO-aligned framework that resolves these inconsistencies, providing the logically sound and interoperable foundation to transform the DPP from an ambitious, innovative vision into a functional, data-driven reality.

The growing complexity and heterogeneity of research data in materials science and engineering (MSE) demand structured and interoperable solutions for effective data management and reuse. To address this challenge, this article introduces the National Research Data Infrastructure (NFDI)‐MatWerk Ontology (MWO), as a semantic foundation to standardize metadata, link distributed datasets, and support digital research data management (RDM) in MSE. MWO addresses the need for the structured, standardized, and semantically rich representation of key entities, processes, and resources involved in the generation, sharing, and reuse of MSE research data. Aligned with the Basic Formal Ontology (BFO) , MWO develops as a modular extension of the NFDIcore ontology, and reuses Platform MaterialDigital core ontology (PMDco) MWO offers broad semantic coverage, modeling elements such as researchers, organizations, projects, software, workflows, datasets, metadata schemas, instruments, events, and services. It supports modular, scalable development through ontology design patterns (ODPs) and is maintained via the Ontology Development Kit (ODK) following best practices from the Open Biomedical Ontologies (OBO) Foundry. MWO also serves as the foundation for the MSE Knowledge Graph (MS‐KG), which integrates semantically interlinked research data across the NFDI‐MatWerk consortium and wider MSE community.

Construction projects still face persistent barriers to adopting whole life costing (WLC), such as fragmented data, a lack of standardization, and inadequate tools. This study addresses these limitations by proposing a core ontology for WLC, developed using an ontology design science research methodology. The ontology formalizes WLC knowledge based on ISO 15686-5 and incorporates professional insights from surveys and expert focus groups. Implemented in web ontology language (OWL), it models cost categories, temporal aspects, and discounting logic in a machine-interpretable format. The ontology’s interoperability and extensibility are validated through its integration with the building topology ontology (BOT). Results show that the ontology effectively supports cost breakdown, time-based projections, and calculation of discounted values, offering a reusable structure for different project contexts. Practical validation was conducted using SQWRL queries and Python scripts for cost computation. The solution enables structured data integration and can support decision-making throughout the building life cycle. This work lays the foundation for future semantic web applications such as knowledge graphs, bridging the current technological gap and facilitating more informed and collaborative use of WLC in construction.

Abstract Modern digital ecosystems require the aggregation of data from multiple tools, but ensuring seamless integration without replicating data across these tools remains a challenge. Dynamic dashboards address this issue by enabling real‐time visualization and interaction with aggregated data. Ontologies, as formalized representations of domain knowledge, offer a robust foundation for achieving this integration. They enable the structuring, querying, and reasoning over complex datasets, ensuring semantic consistency and interoperability across domains. Building on the University of Arizona Ontology Stack (UAOS), a layered, modular ontology system developed to support digital engineering, we explore its application in dynamic dashboard generation. The UAOS leverages the Basic Formal Ontology (BFO) and includes core ontologies based on the Common Core Ontologies and Provenance Notation (PROV‐N), as well as domain‐specific ontologies such as the System Architecture Ontology and the Orbits and Trajectories Ontology. By federating knowledge across these ontologies, the UAOS enables dynamic mapping of heterogeneous data to reusable, semantically coherent structures. This capability facilitates the creation of dashboards that adapt to evolving user needs and data contexts in a tool‐agnostic manner. In this paper, we present a methodology for leveraging the UAOS in the generation of dynamic dashboards. We demonstrate how the integration of ontologies, reasoners, and SPARQL queries supports the automated configuration of dashboard components. Use cases from digital engineering research are discussed to illustrate the approach. Additionally, we address challenges, including real‐time performance and ontology alignment, and identify opportunities for future enhancements in dynamic visualization systems.

Abstract Since the introduction of Digital Engineering (DE) as a well‐defined concept in 2018, organizations and industry groups have been working to interpret the DE concepts to establish consistent meta‐models of those interrelated concepts for integration into their DE processes and tools. To reach the breadth and depth of DE concept definitions, the interpretation of international standard sources is necessary, including ISO/IEC/IEEE 15288, 24765, 42000‐series, 15408, 15206, 27000‐series, and 25000‐series, to effectively model the knowledge domain where digital engineering applies. The harmonization of the concepts used in these international standards continues to improve with each revision, but it may be more effectively accomplished by relying on the descriptive logic formalized in the Web Ontology Language (OWL 2 DL). This paper presents a verified and consistent ontology based on the Basic Formal Ontology (BFO) and Common Core Ontologies (CCO) that defines Seamless Digital Engineering as a digital tooling paradigm that relies on formal verification of digital interfaces to provide a system‐level qualification of the assured integrity of a Digital Engineering Environment. The present work defines classes and equivalence axioms, while using only the BFO‐ and CCO‐defined object properties that relate them, to provide a baseline analysis that may inform future DE‐related ontology development, using a case study to formally define the ‘seamless’ quality in relation to the updated ISO 25010 SQuaRE product quality model. We identified ISO meta‐model inconsistencies that are resolvable using the BFO/CCO ontological framework, and define ‘seamless’ as both a system integration quality and a Human‐Computer Interface quality‐in‐use, working to disambiguate this concept in the context of DE.

Ongoing digitalization and data-driven developments in materials science and engineering (MSE) emphasize the growing importance of reusing research data and enabling machine accessibility, which requires robust data management and consistent semantic data representation. Ontologies have emerged as powerful tools for establishing interoperable and reusable data structures from inconsistent data structures. Despite advancements in semantic data representation for specific applications, integrating application ontologies with primary data repositories, such as electronic lab notebooks (ELNs), to feed world data remains an open task. As a use case in the MSE domain, this work presents a system based on semantic technologies from the point of view of engineers, developed with the help of information scientists, and unraveled on a small scale. The development of an application ontology (AO) was elaborated for flame spray pyrolysis (FSP) processes with the implementation of a data pipeline. The proposed FSP application ontology emerges from experimental in-house best-practice procedures and is adapted to the mid-level Project Material Digital core ontology (PMDco) to allow interoperability within the MSE domain. The pipeline retrieves manually acquired experimental data from an ELN, translates it into a machine-actionable format, and converts it into a Resource Description Framework (RDF) format to support semantic interoperability. The latter was stored in a triple store with a SPARQL interface, enabling findable and accessible datasets that are searchable and traceable. By creating semantically linked data structures in line with FAIR principles, this approach allows traceable and findable experimental results between stakeholders through both human-readable and machine-actionable formats. Seamless integration of the modular microservices of the data pipeline within established lab practices minimizes disruption while maintaining the software framework. The present work demonstrates the practical implementation of a FAIR data pipeline within a laboratory setting, paving the way for future data-centric science.

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Ongoing digitalization and data-driven developments in materials science and engineering (MSE) emphasize the growing importance of reusing research data and enabling machine accessibility, which requires robust data management and consistent semantic data representation. Ontologies have emerged as powerful tools for establishing interoperable and reusable data structures from inconsistent data structures. Despite advancements in semantic data representation for specific applications, integrating application ontologies with primary data repositories, such as electronic lab notebooks (ELNs), to feed world data remains an open task. As a use case in the MSE domain, this work presents a system based on semantic technologies from the point of view of engineers, developed with the help of information scientists, and unraveled on a small scale. The development of an application ontology (AO) was elaborated for flame spray pyrolysis (FSP) processes with the implementation of a data pipeline. The proposed FSP application ontology emerges from experimental in-house best-practice procedures and is adapted to the mid-level Project Material Digital core ontology (PMDco) to allow interoperability within the MSE domain. The pipeline retrieves manually acquired experimental data from an ELN, translates it into a machine-actionable format, and converts it into a Resource Description Framework (RDF) format to support semantic interoperability. The latter was stored in a triple store with a SPARQL interface, enabling findable and accessible datasets that are searchable and traceable. By creating semantically linked data structures in line with FAIR principles, this approach allows traceable and findable experimental results between stakeholders through both human-readable and machine-actionable formats. Seamless integration of the modular microservices of the data pipeline within established lab practices minimizes disruption while maintaining the software framework. The present work demonstrates the practical implementation of a FAIR data pipeline within a laboratory setting, paving the way for future data-centric science.

Aim/Purpose: The paper introduces and develops the Core Ontology for Customs Procedures (COCP), a modular and scalable knowledge model designed to address the complexities of customs operations by formally representing operational, regulatory, security, transport, and financial transaction knowledge in alignment with global standards. Background: Customs authorities face increasing challenges related to evolving regulations, inconsistent documentation, and the lack of interoperability in existing systems. While some ontologies exist, they are often domain-specific and fail to provide a unified structure capable of supporting the breadth of customs activities and automation needs. COCP responds to this gap by offering a comprehensive and integrative solution. Methodology: COCP was developed using the NeOn scenario-based methodology, which supports iterative development and resource reuse. The ontology went through multiple phases including requirements specification based on competency questions, structured knowledge acquisition from authoritative sources, formal implementation in OWL using Protégé, axiomatization of semantic rules, and validation through reasoning tools, question-based testing, and SPARQL-based real-world scenarios. Contribution: The paper contributes a formalized and validated ontology that unifies key customs processes and ensures semantic consistency across modules. It incorporates internationally recognized models such as the World Customs Organization (WCO) Data Model and Harmonized System (HS) Codes, allowing it to function as a foundation for legal compliance, operational efficiency, and AI integration. COCP is structured for modularity, making it adaptable and extendable to changing regulatory and technical environments. Findings: COCP helps standardize customs procedures by promoting consistent data exchange, goods classification, and declaration handling across borders. It supports legal compliance and risk management through formalized rule definitions and reasoning mechanisms. The ontology also facilitates integration with intelligent technologies by providing machine-readable structures. Recommendations for Practitioners: Customs authorities and operational stakeholders are advised to adopt COCP to automate customs clearance, ensure uniform regulatory compliance, and integrate intelligent tools for decision support. The ontology's standardized structure can improve coordination among actors and reduce procedural delays. Recommendation for Researchers: Researchers are encouraged to expand COCP’s application to specialized customs domains, such as trade sanctions, bonded zones, or e-commerce-related imports. Opportunities also exist to explore its integration with machine learning and natural language processing for automated knowledge updates and deeper analytics. Impact on Society: The implementation of COCP can lead to faster, more transparent, and legally compliant customs processes, reducing friction in global trade and enhancing public trust in customs governance. By supporting streamlined procedures and intelligent automation, the ontology contributes to more effective and secure international commerce. Future Research: Future directions include extending COCP to region-specific and domain-specific customs contexts, strengthening its interoperability with diverse platforms, and incorporating AI-driven reasoning systems for advanced automation. Ensuring the ontology remains adaptable to continuous legal and procedural changes will be essential for sustaining its value in global customs environments.

Purpose Addressing integrity, flexibility and interoperability challenges in enterprise information systems (EISs) is often hindered by the “three-fit” barrier, which encompasses vertical, horizontal and transversal fit problems. To overcome these obstacles, we propose solutions aimed at defining the business view of EIS. This study addresses these issues by proposing solutions tailored to the business view of EIS. Specifically, it introduces the core ontology of sensitive business processes (COSBP), a conceptual framework designed to formalize and define the multidimensional dimensions of sensitive business processes (SBPs). By providing a unified structure of central concepts and semantic relationships, COSBP enhances both knowledge management (KM) and business process management (BPM) in organizational contexts. Design/methodology/approach This paper adopts the design science research methodology covering the phases of a design-oriented research project that develops new artifacts, such as the COSBP ontology, based on SBP modeling requirements. Following a formal multi-level, multi-component approach, COSBP is structured into sub-ontologies across different abstraction levels. Built upon the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) foundational ontology, COSBP integrates and extends core concepts from core domain ontologies in business processes. The framework specifies six key modeling dimensions of SBPs – functional, organizational, behavioral, informational, intentional and knowledge – each represented as a distinct class of ontological modules (OMs). Findings COSBP offers a semantically rich and precise framework for modeling SBPs, addressing complexity and ambiguity in conceptual modeling. It supports the creation of expressive and effective SBP models while enabling consensus-driven representation at a generic level. Additionally, COSBP serves as a foundation for extending modeling notations and developing tools that align with these notations. Its application in enterprise environments improves the integration, adaptability and interoperability of EISs, ultimately enhancing organizational processes and decision-making. Originality/value The development of the COSBP ontology holds considerable potential for application in various industries beyond its original focus on business process management and KM. The ontology’s capability to semantically model sensitive, knowledge-intensive and dynamic processes can be extended to other real-life scenarios in other complex domains and sectors – for example, finance and banking, government and public services, insurance, manufacturing and supply chain management, retail, E-commerce, logistics and transportation crisis management, government and public services, higher education and so on. By integrating artificial intelligence (AI) with the COSBP ontology, we aim to enable more intelligent decision-making, process monitoring and improved management of SBPs in knowledge-driven domains.

“Exercise is medicine” emphasizes personalized prescriptions for better efficacy. Current guidelines need more support for personalized prescriptions, posing scientific challenges. Facing those challenges, we gathered data from established guidelines, databases, and articles to develop the Exercise Medicine Ontology (EXMO), intending to offer comprehensive support for personalized exercise prescriptions. EXMO was constructed using the Ontology Development 101 methodology, incorporating Open Biological and Biomedical Ontology Foundry principles. EXMO v1.0 comprises 434 classes and 9,732 axioms, encompassing physical activity terms, health status terms, exercise prescription terms, and other related concepts. It has successfully undergone expert evaluation and consistency validation using the ELK and JFact reasoners. EXMO has the potential to provide a much-needed standard for individualized exercise prescription. Beyond prescription standardization, EXMO can also be an excellent tool for supporting databases and recommendation systems. In the future, it could serve as a valuable reference for developing sub-ontologies and facilitating the formation of an ontology network.

The Common Core Ontologies(CCO) are designed as a mid-level ontology suite that extends the Basic Formal Ontology. In 2017,CUBRC, Inc. made CCO openly available. CCO has since been increasingly adopted by a broad group of users and applications and is proposed as the first standard mid-level ontology. Despite these successes, documentation of the contents and design patterns of the CCO has been comparatively minimal. This paper is a step toward providing enhanced documentation for the mid-level ontology suite through a discussion of the contents of the eleven ontologies that collectively comprise the Common Core Ontology suite.

Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li‐ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.

Indicators are quantitative or qualitative measures used to gauge various aspects of society and assess change over time (such as monitoring the progress or effectiveness of a public policy). Ideally, indicators should be precisely defined and measured according to harmonized procedures that may not be feasible in practice, especially in domains such as health, where indicators are often derived from preexisting, heterogeneous datasets. Integrating such data has posed a persistent challenge, but semantic technologies offer advantages by enriching data in a relatively simple, linkable, and non-disruptive way. However, without harmonized frameworks, the difficulties associated with data integration are unlikely to be resolved. In this article, we propose a generic, domain-neutral indicator contextualization framework for structuring and linking distributed datasets with contextual metadata according to a standard model. The framework integrates the concepts of the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 11179 metadata registry standard with the common core ontologies (CCO) mid-level ontology suite, and incorporates other semantic technologies to make it adaptable and interoperable within and across domains. Application of the framework to an example indicator illustrates the versatility and adaptability of the approach in a federated data architecture. The contextual information can be dereferenced using standard query tools to provide data users a comprehensive understanding and overview of the indicator. The framework is amenable to deep learning applications via the principles of semantic data models, linked open data, and knowledge organization systems. The ideas are presented to stimulate further reflection and consolidation of standard data contextualization frameworks.

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).

The COVID CIRCLE initiative Research Project Tracker by UKCDR and GloPID-R and associated living mapping review (LMR) showed the importance of sharing and analysing data on research at the point of funding to improve coordination during a pandemic. This approach can also help with research preparedness for outbreaks and hence our new programme the Pandemic Preparedness: Analytical Capacity and Funding Tracking Programme (Pandemic PACT) has been established. The LMR described in this protocol builds on the previous UKCDR and GloPID-R COVID-19 Research Project database with addition of the priority diseases from the WHO Blueprint list plus initial additions of pandemic influenza, mpox and plague. We capture data on new funding commitments directly from funders and map these against a core ontology (aligned to existing research roadmaps). We will analyse regularly collated new research funding commitments to provide an open, accessible, near-real-time overview of the funding landscape for a wide range of infectious disease and pandemic preparedness research and assess gaps. The periodicity of updates will be increased in the event of a major outbreak. We anticipate that this LMR and the associated online tool will be a useful resource for funders, policy makers and researchers. In the future, our work will inform a more coordinated approach to research funding by providing evidence and data, including identification of gaps in funding allocation with a particular focus on low- and middle-income countries.