Search Results (267)

Strategic logistics planning is used by management to define action plans that will enable organizations to always make decisions that are in the organization’s best interests. They are based on a knowledge repository of business experiences, which is usually represented by a centralized, organized, and searchable digital system where organizations store and manage critical institutional knowledge. Thus, an institutional knowledge base provides sustainability, making the experiences readily available while keeping them well organized. In this research, the experiences of logistics experts from selected scholarly designs for six-sigma business improvement projects have been collected, classified, and organized to form a logistics knowledge management system. Although originally meant to facilitate current and future decisions in strategic logistics planning of the cooperating companies, it is also used in logistics education to introduce knowledge-based engineering principles to enterprise strategic planning, based on continuous improvement of quality-related product or process performance indicators. The main goal of this article is to highlight the benefits of knowledge-based engineering over the established ontological logistics knowledge base in smart production, based on the predisposition that ontological institutional knowledge base management is more efficient, adaptable, and sustainable. Full article

Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces in autonomous machines was developed using ChatGPT-4o. Based on this case study, the results were generalized into a reusable methodology. The proposed workflow integrates classical ontology engineering methodologies with the generative and analytical capabilities of LLMs. Each phase follows well-established steps: domain definition, term elicitation, class hierarchy construction, property specification, formalization, population, and validation. A key innovation of this approach is the use of a guiding table that translates domain knowledge into structured prompts, ensuring consistency across iterative interactions with the LLM. Human experts play a continuous role throughout the process, refining definitions, resolving ambiguities, and validating outputs. The ontology was evaluated in terms of logical consistency, structural properties, semantic accuracy, and inferential completeness, confirming its correctness and coherence. Additional validation through SPARQL queries demonstrated its reasoning capabilities. This methodology is generalizable to other domains, if domain experts adapt the guiding table to the specific context. Despite the support provided by LLMs, domain expertise remains essential to guarantee conceptual rigor and practical relevance. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

Extensive research in the field of construction safety has predominantly focused on identifying the causes and impacts of construction accidents, evaluating safety plans, assessing the effectiveness of safety education materials, and analyzing relevant policies. However, comparatively limited attention has been given to the systematic formation, management, and utilization of safety-related information and knowledge. Despite significant advancements in information and knowledge management technologies across the architecture, engineering, and construction (AEC) industries, their application in construction safety remains underdeveloped. This study addresses this gap by proposing a novel ontology-based framework specifically designed for construction safety management. Unlike previous models, the proposed ontology integrates diverse safety regulations and terminologies into a unified and semantically structured knowledge model. It comprises three primary superclasses covering key areas of construction safety, with an initial focus on fall hazards—one of the most frequent and severe risks, particularly in roofing activities. This domain-specific approach not only improves semantic clarity and standardization but also enhances reusability and extensibility for other risk domains. The ontology was developed using established methodologies and validated through reasoning tools and competency questions. By providing a formally structured, logic-driven knowledge base, the model supports automated safety reasoning, facilitates communication among stakeholders, and lays the foundation for future intelligent safety management systems in construction. This research contributes a validated, extensible, and regulation-aligned ontology model that addresses critical challenges in safety information integration, sharing, and application. Full article

Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830–1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries—including LLM-generated queries—to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering. Full article

The recent birth of genetically modified canids phenotypically resembling the extinct dire wolf (Aenocyon dirus) was hailed as a landmark in synthetic biology. Using genome editing and cloning, the biotech company Colossal Biosciences created three such animals from gray wolf cells, describing the project as an effort in “functional de-extinction”. This case raises significant questions regarding animal welfare, moral justification, and regulatory governance. We used the five domains model framework to assess the welfare risks for the engineered animals, the surrogate mothers used in reproduction, and other animals potentially affected by future reintroduction or escape scenarios. Ethical implications are examined through utilitarian, deontological, virtue, relational, and environmental ethics. Our analysis suggests that the project suffers from ontological ambiguity: it is unclear whether the animals created are resurrected species, hybrids, or novel organisms. While the current welfare of the engineered animals may be manageable, their long-term well-being, particularly under rewilding scenarios, is likely to be compromised. The moral arguments for reviving long-extinct species are weak, particularly in cases where extinction was not anthropogenic. Legally, the current EU frameworks lack the clarity and scope to classify, regulate, or protect genetically engineered extinct animals. We recommend that functional de-extinction involving sentient beings be approached with caution, supported by revised welfare tools and regulatory mechanisms. Full article

The AECO (Architecture, Engineering, Construction, and Operation) sector is highly complex, involving multidisciplinary collaboration, extensive data management, and significant financial investments. Decisions in early phases significantly impact operational and maintenance costs, as well as the environmental and economic sustainability of a project over its lifecycle. Cost efficiency and sustainability are critical and interconnected goals across the sector, spanning all phases of a building’s lifecycle. Ontologies, as formal and structured representations of knowledge within a particular domain, have the potential to enhance cost efficiency by improving decision-making, reducing redundancies, and optimizing resource allocation. Despite their relevance, cost ontologies are still lacking in the AECO sector. This paper addresses this gap by presenting both a methodological and conceptual contribution: it outlines a structured and iterative methodology for developing a cost ontology, and it defines the core concepts required to semantically represent construction cost information. The methodology emphasizes stakeholder engagement and refinement cycles, while the ontological structure ensures machine-readability and interoperability. The approach involves a preliminary analysis of the necessary cost parameters for defining the ontology and a subsequent validation of a practical case study. The results show the development of a heterogeneous and standardized data structure designed to define a cost ontology, aimed at improving the updatability, transparency, and sustainability-oriented interpretation of construction cost data by both humans and machines. Full article

Construction projects are becoming increasingly complex due to their dynamic nature, the integration of multiple disciplines, and the need for strategic alignment between organizational processes and project management. However, traditional project management approaches often fail to address this complexity effectively. This study presents the application of IModel, a web-based semantic model grounded in systems thinking, designed to integrate enterprise architecture and project management. Through a case study conducted in a multinational AEC company, IModel was evaluated for its ability to enhance system interoperability, optimize processes, and support strategic decision-making. The methodology combined web semantic modeling with expert interviews and organizational data analysis. Findings indicate that IModel provides a comprehensive framework for knowledge management, reduces uncertainty, and improves decision-making in dynamic project environments. However, challenges related to model adoption, including the need for training in systems thinking and ontological modeling, were identified. This study contributes to the literature on innovation in construction project management, highlighting the potential of systems thinking and semantic tools to address complex problems in dynamic and evolving environments. Full article

Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. Full article

Ontology development is a multidisciplinary work involving domain experts and knowledge engineers. Bringing together such a team to develop an ontology of quality is not easy. Therefore, ontologies are often created with limited expertise either in the medical domain or in ontology engineering. Unfortunately, the existing methodologies do not provide much guidance on how the different steps of ontology development should be performed, particularly in the case of reduced involvement of domain experts. This challenge is getting more difficult when there is a multitude of medical knowledge sources and ontologies covering parts of the domain, and often, each has a different representation of the same concept, for example, as a symptom, disease, or clinical sign. This research presents a methodology for creating a medical ontology of quality with limited involvement of the domain experts. The latter are only consulted in the domain definition and evaluation phases. We combine building an ontology from codified knowledge and ontology reuse to enhance reusability and interoperability. The methodology is inspired by METHONTOLOGY, for which we make several improvements, especially in the ontology reuse phase. We provide proof of concept of the proposed methodology with a case study involving the development of the pneumonia diagnosis ontology (PNADO). Full article

Escherichia coli and Klebsiella pneumoniae are major contributors to the global challenge of antimicrobial resistance, posing serious threats to public health. Among current preventive strategies, conjugate vaccines that utilize bacterial surface polysaccharides have emerged as a promising and effective approach to counter multidrug-resistant strains. In this study, both the Wzy/Wzx-dependent and ABC transporter-dependent biosynthetic pathways for antigenic polysaccharides were introduced into E. coli W3110 cells. This dual-pathway engineering enabled the simultaneous biosynthesis of two structurally distinct polysaccharides within a single host, offering a streamlined and potentially scalable strategy for vaccine development. Experimental findings confirmed that both polysaccharide types were successfully produced in the engineered strains, although co-expression levels were moderately reduced. A weak competitive interaction was noted during the initial phase of induction, which may be attributed to competition for membrane space or the shared use of activated monosaccharide precursors. Interestingly, despite a reduction in plasmid copy number and transcriptional activity of the biosynthetic gene clusters over time, the overall polysaccharide yield remained stable with prolonged induction. This suggests that extended induction does not adversely affect final product output. Additionally, two glycoproteins were efficiently generated through in vivo bioconjugation of the synthesized polysaccharides with carrier proteins, all within the same cellular environment. This one-cell production system simplifies the workflow and enhances the feasibility of generating complex glycoprotein vaccines. Whole-cell proteomic profiling followed by MFUZZ clustering and Gene Ontology analysis revealed that core biosynthetic genes were grouped into two functional clusters. These genes were predominantly localized to the cytoplasm and were enriched in pathways related to translation and protein binding. Such insights not only validate the engineered biosynthetic routes but also provide a molecular basis for optimizing future constructs. Collectively, this study presents a robust synthetic biology platform for the co-expression of multiple polysaccharides in a single bacterial host. The approach holds significant promise for the rational design and production of multivalent conjugate vaccines targeting drug-resistant pathogens. Full article

Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article

Aiming at the efficiency bottleneck and error risk caused by the over-reliance on manual experience in traditional assembly sequence planning, the urgent demand for deep reuse of multi-source knowledge in complex products, and the growing demand for resource saving and sustainable development, this study focuses on the core problem of the lack of empirical knowledge modeling and reasoning mechanism in the assembly process of complex products, and proposes a three-phase assembly sequence intelligent planning method that integrates deep learning and ontology theory. Method: First, we propose an instance segmentation model based on the improved Mask R-CNN architecture, incorporate the ResNet50 pre-training strategy to enhance the generalization ability of the model, reconstruct the Mask branch, and add the attention mechanism to achieve high-precision recognition and extraction of geometric features of the assembly parts. Secondly, a multi-level assembly ontology semantic model is constructed based on the ontology theory, which realizes the structured expression of knowledge from three dimensions: product structure level (product–assembly–part), physical attributes (weight/precision/dimension), and assembly process (number of fits/direction of assembly), and builds a reasoning system with six assembly rules in combination with the SWRL language, which covers the core elements of geometric constraints, process priority, and so on. Finally, experiments are carried out with the example gearbox as the validation object, and the results show that the assembly sequence generated by the method meets the requirements of the process specification, which verifies the validity of the technology path. By constructing a closed-loop technology path of “visual perception–knowledge reasoning–sequence generation”, this study effectively overcomes the subjective bias of manual planning, integrates multi-source knowledge to improve the reuse rate of knowledge, and provides a solution of both theoretical value and engineering feasibility for the intelligent assembly of complex electromechanical products, which reduces the R&D cost and contributes to the sustainable development. Full article

This study examines the key obstacles that hinder the mass adoption of additive manufacturing (AM) processes for fabrication and processing of metal parts. To address these challenges, the necessity of integrating an intelligent decision support system (DSS) into the workflow of AM process engineers is demonstrated. The advantages of applying a two-level ontological approach to the creation of semantic information to develop an ontology-based DSS are pointed out. A key feature of this approach is that the ontological models are clearly separated from data and knowledge bases formed on this basis. An ensemble of ontological models is presented, which is the basis for the intelligent DSS being developed. The ensemble includes ontologies for equipment and materials reference databases, a library of laser processing technological operation protocols, knowledge base of settings used for laser processing and for mathematical model database. The ensemble of ontological models is implemented via the IACPaaS cloud platform. Ontologies, databases and knowledge base, as well as DSS, are part of the laser-based AM knowledge portal, which was created and is being developed on the platform. Knowledge and experience obtained by various technologists and accumulated within the portal will allow one to lessen a number of extensive trial-and-error experiments to find suitable processing settings. In the long term, the deployment of this portal is expected to reduce the qualification requirements for AM process engineers. Full article

The Microservice Architecture Style (MSA) has emerged as a significant computing paradigm in software engineering, with companies increasingly restructuring their monolithic systems to enhance digital performance and competitiveness. However, the migration process, particularly the microservice identification phase, presents complex challenges that require careful consideration. This study aimed to provide developers and researchers with a practical roadmap for microservice identification during legacy system migration while highlighting crucial migration steps and research requirements. Through a systematic mapping study following Kitchenham and Petersen’s guidelines, we analyzed various microservice identification approaches and developed a middleweight ontology that can be queried for key inputs, data modeling, identification algorithms, and performance evaluation metrics. Our research makes several significant contributions: a comprehensive analysis of existing identification methodologies, a multi-dimensional framework for categorizing and evaluating approaches, an examination of current research trajectories and literature gaps, an ontological framework specifically designed for microservice identification, and an outline of pressing challenges and future research directions. The study concluded that microservice identification remains a significant barrier in system migration efforts, highlighting the need for more research focused on developing effective identification techniques that consider various aspects, including roles and dependencies within a microservice architecture. This comprehensive analysis provides valuable insights for professionals and researchers working on microservice migration projects. Full article