Digital twin (DT) development has created a significant demand for digital model generation. However, this rapid proliferation introduces a novel challenge: model silos across heterogeneous systems and lifecycle phases. Integrating increasingly complex, multidisciplinary models developed by various stakeholders remains difficult. Addressing interoperability issues is essential to ensure consistent communication and data exchange among heterogeneous digital models. This paper proposes an ontology-based methodology on a Semantic Hub architecture to enhance interoperability between system design and discrete event simulation models.The Semantic Hub integrates reference ontologies and semantic parsing to provide unified semantic representations across modeling tools and lifecycle contexts. The applicability of the proposed method is assessed through case studies. Results show that the approach enables automated generation of semantically consistent knowledge graphs and supports cross-tool model shift with reduced manual intervention. Accordingly, the framework preserves domain knowledge while ensuring consistent representation across design and simulation models. By bridging semantic gaps between these models, this study provides a semantic foundation for lifecycle-oriented interoperability in digital twin development.

Geological knowledge is fundamentally interpretive, yet the semantic infrastructure meant to formalize it remains fragmented, unevenly preserved, and insufficiently characterized. This survey applies a stratified evaluation framework to 28 geoscientific semantic artefacts spanning axiomatized OWL ontologies, schema-based data models, and literature-only resources. Available ontologies are evaluated through two complementary axes: intrinsic quality, combining FAIR-conformance auditing with structural and hierarchical metrics, and ontological interoperability, analysed through a layered interoperability pyramid. Schema-based data models are examined through a dedicated schema-level assessment protocol, while orphaned resources are analysed through a proxy evaluation procedure, revealing that 64.3

Modern manufacturing systems operate in highly dynamic environments, where continuous innovation demands extensive monitoring and adaptation. Bottlenecks in these systems pose challenges for testing and implementing improvements without disrupting ongoing operations. Discrete Event Simulation (DES) offers a forward-looking methodology for assessing alternative scenarios within complex systems. However, the manual design of DES models is typically resource- and time-intensive, making it difficult to accommodate dynamic, rapidly changing conditions. The current approaches are largely conceptual and do not incorporate evidence-based DES model building. This limits their effectiveness and scalability, particularly in modern manufacturing environments characterized by high variability and constant evolution. This study targets the complexity of manually designing DES environments by proposing an automated framework—WEFTSIM—that extracts simulation models directly from manufacturing data. The goal is to enhance the accuracy, efficiency, and adaptability of DES models in representing real-world processes. The evaluation combines two approaches: (i) A real-world case-study-driven assessment of WEFTSIM’s applicability to the automatic, data-driven design of DES models through a systematic comparison of scenario performance. (ii) Validation assessment through a comparative performance analysis of the proposed methods against historical data to quantify their fidelity in representing the system behavior. WEFTSIM effectively derived DES models that closely mirror the actual manufacturing operations, attaining around 80% coverage at the activity level and achieving a high trace similarity of 90% when validated against the observed data. The automated approach reduced the manual and time-intensive conceptualization phase. The evaluation against existing benchmark methods shows WEFTSIM’s capability to automatically design DES models, detect bottlenecks, and rapidly identify an improvement scenario.

Since the emergence of the Semantic Web concept, considerable work has focused on service composition using ontology-based approaches. Meanwhile, the concept of Industry 4.0 has emerged, emphasizing the benefits of utilizing data and computing devices in close proximity to production lines, exemplified by concepts like digital twins. However, these two fields rarely intersect, and the requirements for integrating domain-specific knowledge into business processes with event feedback during processes execution differ between these contexts. With the recent advancements in the semantization of industrial standards, such as the Asset Administration Shell, this work explores the elements of a semantic model for describing equipment, enabling the semantic composition of equipment as services. We propose an ontology, COMPAAS, designed to facilitate the composition of production lines that can react to events reported by their components, allowing the system to adjust its behavior accordingly. This approach also addresses the removal and addition of hardware or software elements within the chain, and the entire concept is validated through a minimal use case that demonstrates the improved flexibility of the production line in response to potential disturbances.

The increasing adoption and investment in Model-Based Systems Engineering (MBSE) and Digital Thread and Digital Twin (DT T) demand interoperability among models across the organization. Model interoperability is a critical challenge in the development of DT T due to the diversity of models and standards used in different phases of product or system design life cycle (PLC/SDLC). Moreover, this challenge is further complicated by the complexity of the differences among various organizations and enterprises. This paper proposes an ontology-driven approach to address the challenge of model interoperability and emphasizes the significance of intra-and cross-organizational Enterprise Integration (EI) and trustworthy decision-making in the development of DT T. The proposed approach investigates models’ interoperability, transformation, and portability and introduces potential research avenues to add explainability throughout the digital model’s lifecycle. Furthermore, the paper emphasizes the need for standard evaluation criteria or measurements to evaluate the maturity of EI and to compare different model translation and portability methodologies.

Digital twins (DT) in manufacturing, healthcare, and across different industrial domains are often over-simplified as solely a virtual representation of a physical object or service. Such a definition constitutes a dilemma in distinguishing DTs from digital models, digital shadows, digital threads, and cyber-physical systems. In this article, we aim to elucidate the concept of digital twins and its definition. Therefore, we go through the connotation of digital twins, which has its roots in space exploration and product-life cycle management, and describe the four evolution stages of DT developments. This article employs an ontological approach to clearly and comprehensively define digital twins and related key concepts, including digital models, assets, prototypes, shadows, and threads. Additionally, it presents a structured framework detailing the meta-model and the reference-level ontology of digital twins. To evaluate the proposed structure, definitions, and its important entities, we have examined our framework against 73 peer-reviewed papers in the healthcare sector from 2018 until July 2024. The evaluation and classification criteria of the selected works were based on four research questions. These criteria are driven by the core definitions provided by the main cross-domain digital twin researchers and this article’s proposed anatomy of digital twins.

The adaptability of robotic systems is expanding the horizons of manufacturing flexibility. However, fully leveraging the potential of these systems poses considerable challenges. A key requirement is the ability to understand and model their diverse capabilities through a standardized and semantically well-defined framework. In this paper, we introduce the Robotic Capability Ontology (RCO), developed through a systematic investigation of various types of robotic capabilities, including those related to function, quality, and process performance. We define two types of capabilities: Advertised capabilities, as specified by manufacturers, and Operational capabilities, which reflect real-world performance. The RCO framework provides an ontology-based approach to representing these capabilities in a structured and interpretable manner. Within the manufacturing context, RCO serves as a reference ontology that bridges manufacturer specifications and empirical performance data to support more accurate, explainable, and interoperable representations of robotic capabilities.

In modern industries, digital models have become indispensable assets for design and simulation. Despite their broad application, interoperability challenges remain an obstacle to effective integration. This paper proposes an ontology-driven approach to address these challenges by bridging semantic gaps among heterogeneous models. The approach consists of a semantic representation of the model’s informational content following an ontology schema and a mapping algorithm that systematically aligns nodes, blocks, and relationships. Its feasibility is validated and evaluated by a case study of parsing and mapping in different scenarios, between the SysML (Systems Modeling Language) system design model and the Discrete Event Simulation model.

Manufacturers often struggle to determine the appropriate processes, machines, and procedures when developing a new product, especially without extensive technical expertise. While knowledge graphs (KGs) help structure and access domain-specific information, existing solutions lack commonsense reasoning to support intuitive and explainable decision-making. This demo presents MACS-KG, a KG designed to assist manufacturers in identifying the right manufacturing processes for a product, selecting suitable machines, and following standardized procedures. By integrating Manufacturing Commonsense Knowledge (MCSK) rules, MACS-KG enables reasoning and explainable decision-making, ensuring that manufacturers access relevant information and understand the rationale behind each recommendation derived using MCSK.

This paper underscores the critical role of integrating common knowledge across the manufacturing industry by introducing and formalizing a new concept called Manufacturing Commonsense Knowledge (MCSK). Although commonsense knowledge is crucial for enhancing AI-driven operational intelligence and decision-making, its structured application within the broader manufacturing sector has been insufficient. To bridge this gap, we present a structured methodology for translating MCSK into first-order logic (FOL), employing standard ontological frameworks such as the Basic Formal Ontology (BFO), Industrial Ontologies Foundry (IOF), Relations Ontology (RO), and Machine Services Description Language (MSDL). This translation process is pivotal, whether the underlying AI systems employ symbolic, sub-symbolic, or hybrid approaches, as it transforms intuitive MCSK into organized semantic rules. Our findings demonstrate that structured MCSK patterns enhance knowledge representation's clarity and utility and significantly improve the explanatory capabilities of AI decision-making processes across the industry. The broader impacts of our research extend to enhancing machine interoperability, predictive analytics, and advanced manufacturing practices, thus paving the way for a more informed and efficient industrial future.

Artificial intelligence (AI) has become an essential tool for manufacturers seeking to optimize their production processes, reduce costs, and improve product quality. However, the complexity of the underlying mechanisms of AI systems can render it difficult for humans to understand and trust AI-driven decisions. Explainable AI (XAI) is a rapidly evolving field that addresses this challenge, providing human-understandable explanations of AI decisions. Based on a systematic literature survey, We explore the latest techniques and approaches that are helping manufacturers gain transparency in the decision-making processes of their AI systems. In this survey, we focus on two of the most exciting areas of XAI: ontology-based and semantic-based XAI (O-XAI, S-XAI, respectively), which provide human-readable explanations of AI decisions by exploiting semantic information. These latter types of explanations are presented in natural language and are designed to be easily understood by non-experts. Translating the decision paths taken by AI algorithms to meaningful explanations through semantics, O-XAI, and S-XAI enables humans to identify various cross-cutting concerns that influence the decisions made by the AI system. This information can be used to improve the performance of the AI system, identify potential biases in the system, and ensure that the decisions are aligned with the goals and values of the manufacturing organization. Additionally, we highlight the benefits and challenges of using O-XAI and S-XAI in manufacturing and discuss the potential for future research, aiming to provide valuable guidance for researchers and practitioners looking to leverage the power of ontologies and general semantics for XAI.

Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.

Over recent decades, the advancement of semantic web technologies has underscored the increasing importance of tools dedicated to developing and managing the foundational components of the semantic web stack. Addressing the evolving needs, a variety of tools have emerged from the research and development projects from academia as well as commercial software vendors. These tools offer a diverse range of services tailored to the management of various aspects of semantic knowledge graphs. Despite this proliferation, feedback from stakeholders involved in public and privately funded projects has highlighted notable shortcomings in existing tools. These gaps become evident in two key areas: firstly, the user experience struggles to scale up to meet industrial-level data practices and knowledge engineering methodologies. Secondly, a lack of interoperability and compatibility among the existing task-specific tools leads to elevated costs and efforts. This paper introduces a novel semantic knowledge management ecosystem embodied in a suite of tools collectively known as ’SousLeSens’. Unlike its counterparts, SLS not only provides comprehensive coverage of typical knowledge engineering tasks while adhering to best practices for ensuring quality but also boasts a purely visual (no to minimum-code) interface. This feature is particularly well-suited for handling large-scale, industry-grade semantic data models. The paper delves into the establishment of requirements for knowledge engineering tools and services, derived from recent stakeholder surveys. It proceeds to present the SLS toolkit, elucidating its architecture and operational protocols. Finally, the paper validates the toolkit’s capabilities by comparing it with existing tools against predefined requirements and illustrating various use cases.

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.

In the realm of Digital Twins (DTs), industry experts have emphasised the pivotal concept of the Federation of Twins, envisioning seamless collaboration across sectors driven by shared semantics. In response to this challenge, the Cognitive Digital Twin (CDT) integrates the DT framework with formal semantics, specifically ontologies. This paper introduces a comprehensive five-step methodology for CDT development. Furthermore, it becomes possible to incorporate human expertise into the DT ecosystem by adopting an ontological approach. The CDT enhances DT services with advanced reasoning capabilities, leading to a profound semantic enrichment of the data. The presented methodology has been validated using a use case where the CDT is employed to detect malfunctions, significantly reducing manual intervention. This paper advocates for the adoption of CDTs, which represent a harmonious fusion of formal semantics and human expertise, enhancing system efficiency and operational performance.