Ontology Model Research Articles

FiReS: A semantic model for advanced querying and prediction analysis for first responders in post-disaster response plans

Natural disasters have emerged as a recurring and severe menace to the sustainability of countries, resulting in compromised environmental and infrastructural integrity, human fatalities, and significant economic repercussions. Prompt and effective response by first responders is essential in Disaster Risk Management (DRM) to reduce individuals' vulnerability and minimize environmental and infrastructural damages. However, disaster-related information is often generated by heterogeneous sources, making the first responders’ decision-making process complex and time-consuming. To address these challenges, a common conceptual model is imperative to improve interoperability among diverse organizations and software systems, enabling effective collaboration. Semantic Web technologies offer a promising solution for integrating heterogeneous data and providing well-defined meaning in the representation and exchange of DRM-related knowledge. In this context, this study introduces FiReS (First Responders System), an ontological model designed to enhance data interoperability among first responders in post-disaster response plans for advanced data analysis and machine learning prediction. The validation of FiReS is conducted through a series of case studies exploring various aspects of disaster response, such as the response time of emergency services and the volume and classification of emergency calls. This approach facilitates streamlined access, thorough analysis, and seamless exchange of information, empowering stakeholders to strengthen their disaster response strategies and foster resilience within communities.

An Ecosystem for the Provision of Digital Accessibility for People with Special Needs

Digital technologies occupy an important place in today’s developing world. They are also strongly related to new trends in educational technologies. In this context, the digital accessibility of this new environment for people with various special needs is of particular concern. A novel tool for assessment of the technological ecosystem, designed to provide digital accessibility to people with special needs, is described in the paper. The overall structure and the initial test of the system are discussed in the paper. The conceptual framework of the ecosystem and its ontological model are described. Special attention is paid to the accessibility of digital learning and e-learning for people with special needs from a robotic perspective.

A Priestly Christology: A Biblical-Theological Investigation of Christ’s Priesthood

The concept of Christ’s Priesthood has been the subject of comprehensive research work in recent times. Despite several attempts to investigate the doctrine of Christ’s Priesthood waves a theological enigma. Thus, the study delved into the concept of Christology as denoted in the Scripture. This scholarly analysis focused on the qualities, which are linked to atonement and mediation. Christian theology has various viewpoints, but the primary model connects the sacrifice of Christ to His role as intercessor. The ontological model was a theological framework that stresses Christ’s priesthood on His personality and activities. The sacramental priesthood model denotes a correlation between Christ’s sacrificial act and His intercession in a heavenly realm, with distinct aspects following a predetermined sequence. The functional priesthood model is based on classical Protestant theology which portrays that the sacrifice of Christ and mediation are intrinsically interconnected with His being. The study employed a biblical-theological approach that sought to address the prevailing debates concerning Christ’s Priesthood. Thus, the study injects relevant sources to formulate and build the Christological concept. The research paper opined a holistic understanding of the nature of Christ and His transformative power to redeem humanity through the unction of the Holy Spirit. It is crystal clear that Christ sacrificed Himself to atone for all humankind’s sins. More importantly, the paper contributes to the current exploration of Priestly Christology, thus fostering a deeper understanding of Christ’s Priesthood and its significance to contemporary biblical theological discussion. It also helps to lay bare the salvific value of ecclesiology. This paper is recommended for biblical-theological historical students to advance and improve their studies. Keywords: Priestly Christology, Biblical-Theological, Evaluation, Christ’s Priesthood

Towards Controllable Generative Design: A Conceptual Design Generation Approach Leveraging the FBS Ontology and Large Language Models

Abstract Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.

Prototyping an Ontological Framework for Cellular Senescence Mechanisms: A Homeostasis Imbalance Perspective

Although cellular senescence is a key factor in organismal aging, with both positive and negative effects on individuals, its mechanisms remain largely unknown. Thus, integrating knowledge is essential to explain how cellular senescence manifests in tissue damage and age-related diseases. Here, we propose an ontological model that organizes knowledge of cellular senescence in a computer-readable form. We manually annotated and defined cellular senescence processes, molecules, anatomical structures, phenotypes, and other entities based on the Homeostasis Imbalance Process ontology (HOIP). We described the mechanisms as causal relationships of processes and modelled a homeostatic imbalance between stress and stress response in cellular senescence for a unified framework. HOIP was assessed formally, and the relationships between cellular senescence and diseases were inferred for higher-order knowledge processing. We visualized cellular senescence processes to support knowledge utilization. Our study provides a knowledge base to help elucidate mechanisms linking cellular and organismal aging.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.

Translating motivational interviewing for the HPV vaccine into a computable ontology model for automated AI conversational interaction.

Human papillomavirus (HPV) vaccinations are lower than expected. To protect the onset of head and neck cancers, innovative strategies to improve the rates are needed. Artificial intelligence may offer some solutions, specifically conversational agents to perform counseling methods. We present our efforts in developing a dialogue model for automating motivational interviewing (MI) to encourage HPV vaccination. We developed a formalized dialogue model for MI using an existing ontology-based framework to manifest a computable representation using OWL2. New utterance classifications were identified along with the ontology that encodes the dialogue model. Our work is available on GitHub under the GPL v.3. We discuss how an ontology-based model of MI can help standardize/formalize MI counseling for HPV vaccine uptake. Our future steps will involve assessing MI fidelity of the ontology model, operationalization, and testing the dialogue model in a simulation with live participants.

A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

The significant impact of the built environment on resource consumption and waste production has led to calls for a shift towards a circular economy model that maximizes the efficient use of resources. This study explores the use of digital product passports (DPPs) to improve how we manage products throughout their lifecycle. However, dealing with the complexity and large volume of data in DPPs can be challenging in terms of effective information management and utilization. We address this issue by adopting a modular ontological approach to systematically capture product lifecycle information from its origin to its end-of-life phase. To ensure interoperability and reusability of the ontology, we annotate key concepts and relationships using International Organization for Standardization (ISO) standards that promote circular economy. Our research led to the development of several ontology modules derived from literature reviews and interviews conducted with industry and academia experts who specialize in sustainability. These modules were then integrated to create a digital product passport ontology. The study demonstrates the feasibility of using a modular ontology approach to manage the complex information inherent in DPPs paving the way for more sustainable management practices in the built environment sector.

Enhancing awareness of industrial robots in collaborative manufacturing

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

An ontology of 3D environment where a simulated manipulation task takes place (ENVON)

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.

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Ontological representation, modeling, and analysis of parasite vaccines

BackgroundPathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed.ResultsVIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, ‘transmission-blocking vaccine,’ was added to represent vaccines able to block infectious transmission, and one new VO object property, ‘blocks transmission of pathogen via vaccine,’ was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling.ConclusionsThe updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.

КОМПЛЕКСНІ ОНТОЛОГІЧНІ ТА НЕЙРОМЕРЕЖЕВІ МОДЕЛІ ФОТОГРАФІЧНИХ ОБРАЗІВ

This article explores an innovative approach to developing computer vision systems, based on the deep integration of complex ontological and neural network models for effective analysis of photographic images. This work proposes a novel hybrid method in the context of contemporary challenges associated with image recognition, such as a high diversity of classes and problems with the occlusion of objects in low-quality photos. This method provides computer vision models with a deeper understanding of the context and semantics of images, utilizing ontological structures to represent visual content. The analytical process includes the identification of key objects in the image and the determination of their ontological context, allowing neural networks to effectively perform a gradient transformation of the input data for more accurate recognition and classification. The proposed model demonstrates the potential for gaining an advantage over traditional methods in computer vision tasks, opening new possibilities for expanding computer vision applications in scientific, industrial, and domestic spheres.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

A knowledge graph – based requirement identification model for products remanufacturing design

The effective identification of remanufacturing design requirements is crucial to ensure that remanufactured products meet the required standards and demands. However, in addition to considering the performance demands of customers for products, remanufactured products should also take into account the various types and degrees of failure across diverse parts. To this end, under the dual demands, a knowledge graph – based requirement identification model is proposed to intelligently transform complex remanufacturing demands into standardised design requirements. Firstly, excavation of design information from representative cases is conducted, including failure characteristics, customer demands and design requirements (FCDR). A FCDR ontology model is proposed by establishing the mapping relationship between demands and design requirements. Secondly, the triplet of design information is integrated into the BERT-BiLSTM-CRF model to obtain the accurate entity, and the ALBERT-BiLSTM-Attention model is used to extract the FCDR relationship, so that a knowledge graph can be constructed. Thirdly, by using coupling weighting technique to consider the relevance of the dual demands, normalised failure characteristics and customer demands are accurately extracted and mapped to similar design requirement nodes in knowledge graph. Finally, a machine tool remanufacturing design is used as an example to verify the effectiveness of the proposed model.

From enterprise models to low-code applications: mapping DEMO to Mendix; illustrated in the social housing domain

Due to hyper-competition, technological advancements, regulatory changes, etc, the conditions under which enterprises need to thrive become increasingly turbulent. Consequently, enterprise agility increasingly determines an enterprise’s chances for success. As software development often is a limiting factor in achieving enterprise agility, enterprise agility and software adaptability become increasingly intertwined. As a consequence, decisions that regard flexibility should not be left to software developers alone. By taking a Model-driven Software Development (MDSD) approach, starting from DEMO ontological enterprise models and explicit (enterprise) implementation design decisions, the aim of this research is to bridge the gap from enterprise agility to software adaptability, in such a way that software development is no longer a limiting factor in achieving enterprise agility. Low-code technology is a growing market trend that builds on MDSD concepts and claims to offer a high degree of software adaptability. Therefore, as a first step to show the potential benefits to use DEMO ontological enterprise models as a base for MDSD, this research shows the design of a mapping from DEMO models to Mendix for the (automated) creation of a low-code application that also intrinsically accommodates run-time implementation design decisions.

The Intelligent System for Low Recourses Languages for Expanding Language Recourses

This research, funded under the BR11765535 “Development of Scientific and Linguistic Foundations and IT Resources to Expand the Functions and Improve the Culture of the Kazakh Language” project, is aimed at the need to develop the scientific and linguistic foundations of the Kazakh language with a special focus on anthroponymy. The "Akhmettanu" intelligent system, which includes digitized materials based on the scientific heritage of Akhmet Baitursynuly, consists of an ontological model of knowledge on the structural layers of the language, a Kazakh language user interface with the function of adding and correcting new digitized materials. Digitized materials based on the scientist's work "Language-Tool" include a new system of knowledge about the sound, word and sentence system of the language. The main aim of this system is to promote the scientific heritage of Akhmet Baitursynuly by promoting the further development of digitalized knowledge and information tools that affect the development of the Kazakh language.

Improving Library Services: From Information Services to Knowledge Services

Digital natives and the alpha-beta generation have different behavior from previous generations when it comes to searching for information. They are more likely to use gadgets in every activity of searching, processing, and sharing information/knowledge. This has implications for how libraries serve them. The importance of appropriate services for digital natives and the alpha-beta generation is because they are the nation’s assets in the future. The SECI model describes how knowledge is disseminated from, by, for, and/or to individuals in an organizational environment. The use of the latest web technology in libraries will further dynamize the organization of knowledge in an institution. This text is in the form of an idea prepared based on a literature review with a qualitative approach, describing how to improve library services from information services to knowledge services. Traditional library services only provide information as desired by the library without any analysis or abstracts beyond the data/information in question. The combination of understanding of ontology, semantics, and SECI models encourages an increase in higher service levels from information services to knowledge services. Knowledge services are developed from a combination of various data/information formats on the same subject linked to each other through multimedia visualization so that users get solutions/knowledge to the problems they are looking for. The development of knowledge services requires several related things, namely: technology architecture design, content, human resources, and future development.
 Keywords: information services, knowledge management, knowledge services, library

Onto-SAGCN: Ontology modeling and spatial attention-based graph convolution networks for aircraft assembly quality prediction

Aircraft assembly is an essential stage in the aircraft manufacturing industry, and the increasing complexity of aircraft functionality has put higher requirements for assembly quality. Various deep learning methods based on image or structural data have been used to predict assembly quality. However, these methods focus more on the structural relationships and interactions between assembled products and are difficult to adapt to scenarios where products and equipment may change. This paper proposes a graph convolutional neural network based on ontology modeling and spatial attention mechanism (Onto-SAGCN) for assembly quality analysis and prediction. Specifically, formal representations of assembly equipment, products, and assembly processes are established using ontologies, enabling the unified modeling of spatial and temporal relationships between entities and resolving the issues of data representation inconsistency. Then, the ontologies are transformed into undirected graphs, where nodes represent entities, node attributes represent feature data, and edges represent relationships or constraints between nodes. Designed to process data from the assembly process, the Onto-SAGCN model is subsequently applied to the assembly of aircraft moving wings. It predicts the diameters of holes by gathering operational data from equipment during the assembly and benchmarks these predictions against other established methods. Experimental outcomes affirm the method's elevated accuracy and dependability in forecasting assembly quality.