Ontology Model Research Articles

A semantic and ontology-based framework for enhancing interoperability and automation in IoT systems

The ever-growing and increasing diversity of IoT ecosystems, the seamless interoperability, scale, and near real-time automation have remained grand challenges. This paper presents a solution enterprise semantic and ontology-based framework to mitigate against these challenges through distributed edge cloud architecture with semantic mapping and ontology models. It seamlessly handles data coordination, protocol reconciliation, and interaction between dynamic devices within heterogeneous IoT environments. In this research paper, work simulation covered 100–500 IoT devices communicating using various protocols, such as MQTT, HTTP, and CoAP. The interoperability success rate of the proposed framework reached up to 98%, and the latency was reduced by 65% through edge processing while keeping the system efficiency at 85% with 500 devices, which proves that the proposed research novel framework is scalable and robust enough for real-world use cases. On top of this, The key research output and innovations involve ontology-based semantic mapping, machine learning models to interpret data, and practical resource usage across edge and cloud layers. The research paper findings reveal real-world applications of the proposed framework for smart cities, healthcare, and industrial IoT. Our ongoing work addresses the integration of AI-based (dynamic) ontologies and applying the framework in real-world deployments for more scalable and adaptable scenarios.

Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph

The enhancement of remote sensing interpretation accuracy for rock strata in complex terrain areas has long been limited by challenges in field validation and the insufficient integration of geological knowledge in traditional spectral–spatial feature selection methods. This study proposes a geological remote sensing interpretation framework that integrates textual geological data, which enhances lithological identification accuracy by systematically combining multi-source geological knowledge with machine learning algorithms. Using a dataset of 2591 geological survey reports and scientific literature, a remote sensing interpretation ontology model was established, featuring four core entities (rock type, stratigraphic unit, spectral feature, and geomorphological indicator). A hybrid information extraction process combining rule-based parsing and a fine-tuned Universal Information Extraction (UIE) model was employed to extract knowledge from unstructured texts. A knowledge graph constructed using the TransE algorithm consists of 766 entity nodes and 1008 relationships, enabling a quantitative evaluation of feature correlations based on semantic similarity. When combined with Landsat multispectral data and digital elevation model (DEM)-derived terrain parameters, the knowledge-enhanced Random Forest (81.79%) and Support Vector Machine (75.76%) models demonstrated excellent performance in identifying rock-stratigraphic assemblages in the study area. While reducing subjective biases in manual interpretation, the method still has limitations. These include limited use of cross-modal data (e.g., geochemical tables, outcrop images) and a reliance on static knowledge representations. Future research will introduce dynamic graph updating mechanisms and multi-modal fusion architectures to improve adaptability across diverse geological lithological and structural environments.

RESNET-50 with ontological visual features based medicinal plants classification

ABSTRACT The proper study and administration of biodiversity relies heavily on accurate plant species identification. To determine a plant’s species by manual identification, experts use a series of keys based on measurements of various plant features. The manual procedure, however, is tiresome and lengthy. Recently, advancements in technology have prompted the need for more effective approaches to satisfy species identification standards, such as the creation of digital-image-processing and template tools. There are significant obstacles to fully automating the recognition of plant species, despite the many current research on the topic. In this work, the leaf classification was performed using the ontological relationship between the leaf features and their classes. This relationship was identified by using the swarm intelligence techniques called particle swarm and cuckoo search algorithm. Finally, these features were trained using the traditional machine learning algorithm regression neural network. To increase the effectiveness of the ontology, the machine learning approach results were combined with the deep learning approach called RESNET50 using association rule. The proposed ontology model produced an identification accuracy of 98.8% for GRNN model, 99% accuracy for RESNET model and 99.9% for the combined model for 15 types of medicinal leaf sets.

A Novel Rule-based Expert System for Early Diagnosis of Bipolar and Major Depressive Disorder

A Novel Rule-based Expert System for Early Diagnosis of Bipolar and Major Depressive Disorder

Knowledge Discovery for Patient Survival in a Clinical Discharge Dataset Using Causal Graph Ontological Framework

Knowledge mining from clinical datasets is a critical task in healthcare as well as other fields. While the existing methods, such as randomized controlled trials (RCT) and other automatic machine extraction, have been helpful, they have become increasingly insufficient to keep pace with time, and robust models are required for clinical decisions. In this paper, we present a new method to address this challenge by using the Causal graph ontological model. Our study used a semi-structured textual clinical discharge dataset from the Statewide Planning and Research Cooperative System (SPARCS) to design and validate the patient survival rate assumptions from the dataset. We extracted the clinical information and organized it according to medically relevant fields for decision-making (Diseases, confounders, treatment, and the survival rate). The initial assumptions model was validated using the conditional independent test (CIT) criteria. The outputs of the LocalTest validation showed that the conceptual assumptions of the causal graph hold since the Pearson correlation coefficient ranges between -1 and 1, the p-value was (>0.05), and the confidence intervals of 95% and 25% were satisfied. Furthermore, we used Shapley values to perform sensitivity analysis on the features. Our analysis showed that two variables, such as gender and diseases, contributed little to the survival rate prediction. Our study concludes that the combination of causal graph ontological framework and sensitivity analysis to discover knowledge from the clinical text could help improve the quality of clinical decisions in the text, remove bias in the assumption in medical applications, and serve as a premise for modelling causal data for natural Language machine learning predictions.

Interactive self-contained compliant structure design supported by multi-objective knowledge inference

ABSTRACT Modern structural design must balance design criteria with increasing objectives like cost minimization, carbon reduction, and stakeholder interests. However, this multi-domain knowledge exists in unstructured forms, such as text, formulas, and tables, and converting it into machine-readable structured knowledge within a unified knowledge framework remains challenging. This paper proposes an ontology-based knowledge modeling and mapping approach to transform unstructured knowledge from design specifications, cost, and carbon emissions into structured knowledge. This approach enables self-containing compliance with structural design standards and supports multi-objective trade-offs. Furthermore, ontology models are transformed into backend services to facilitate interactive design. The developed system has been rigorously tested and validated through case studies. This method promotes the standardization, intelligence, and sustainability of the structural engineering and construction industries, significantly enhancing the overall efficiency and collaboration within the sector.

Mathematical model of the business process of higher education institutions based on ontological analysis

Improving the quality of the educational process and bringing it up to modern standards is a complex task that requires a systematic approach and the devel-opment of a strategy for improving the quality of service and efficiency. This work examines the specifics of information modeling within an ontological approach to distributed information environments, as well as the essence of ontological analysis of management business processes for the educational process in higher education institutions. An ontological analysis of modeling business processes in higher education institutions and an analysis of the structure, functions, and information flows of organizing and managing educational activities based on a structural approach are conducted. Competence, as an integrated characteristic of the graduate՚s quality, is considered a result of education. The use of an ontological model of interaction between subjects and objects of the system allows for the evaluation of the system՚s effective-ness and the construction of a management model, enabling the forecasting of critical business processes that may lead to a decrease in decision-making efficiency and disruptions in the functioning of higher education institutions and their units. The paper proposes a concept for the mathematical modeling of ontology and optimization of business processes in higher education institutions under conditions significant for their design. The structural model of the business process proposed in the paper includes both production and service components. The proposed models allow for mathematical modeling and optimization of business processes without constraints on their complexity, as well as the creation of software complexes based on them for numerical (non-descriptive) design of business processes.

Predicting Online Learners’ Performance Through Ontologies: A Systematic Literature Review

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners’ performance through ontological machine-learning techniques and, using a systematic approach, identifies the existing methodologies and tools used to forecast students’ performance. In addition, the environment for generating ontologies, as considered by academics in the field, is likewise identified. Based on the inclusion criteria and by adopting PRISMA as a research methodology, seven studies and two systematic reviews were selected. The findings reveal a scarcity of research devoted to ontologies in the prediction of learners’ achievement. However, the research outcomes suggest that building an ontological model to harness machine-learning capabilities could help accurately predict students’ academic performance. The results of this systematic review are useful for higher education institutes and curriculum planners. This is especially pertinent in online learning settings to avoid dropout or failure. Also highlighted in this study are numerous possible directions for future research.

Ontology-based automated knowledge identification of safety risks in Metro shield construction

ABSTRACT Safety risk identification of metro tunnel construction is a knowledge-intensive process involving various stakeholders and communities. Currently, safety risk information related to decision making in metro tunnel construction is ill-structurally stored in various disordered formats, which hinders knowledge sharing and reuse. This study develops a domain ontology to formalize safety risk knowledge in metro tunnel construction to support safety risk identification. The method integrates ontology and rules inference technology, utilizing semantic inference hazards and corresponding measures to promote security management. An ontology development method with five steps is adopted. The domain ontology organizes safety risk knowledge into five unified classes (i.e. projects, construction activities, precursor information, risk events risk prevention measures). Defined classes, together with corresponding properties and relations, are coded using the Protégé platform. The domain ontology is evaluated theoretically and practically, using criteria-based and application-based evaluations respectively. Finally, a shield axial deviation example was selected to verify the theoretical and technical feasibility of developing the ontology model. The results show that the operation results of the ontology model are consistent with the thinking of safety managers. This approach creates a holistic knowledge base that integrates a wide range of domain knowledge. The model can facilitate real-time hazard identification and prevention at construction sites, providing important guidance and decision support to construction managers.

Beyond Correlation to Causation in Hunter–Gatherer Ritual Landscapes: Testing an Ontological Model of Site Locations in the Mojave Desert, California

Why are rock art sites found in certain places and not others? Can locational or environmental variables inform an understanding of the function and meaning of the art? How can we move beyond observed patterning in spatial associations to a credible explanation of such meanings and ensure that we are not confusing correlation with causation? And what variables were most relevant in influencing site locational choices? These and related problems, whether recognized or not, are the subtext of the last three decades of rock art site distributional and landscape studies. They are now especially important to resolve given the need for accurate predictive modeling due to the rapid transformation of certain regions from undeveloped rural areas into rural industrial landscapes. Partly with this problem in mind, Whitley developed a descriptive model that provides an explanation for the location of Native Californian rock art in the Mojave Desert. It identifies the variables most relevant to site locations based on ethnographic Indigenous ontological beliefs about the landscape. These concern the geographical distribution of supernatural power and its association with certain landforms, natural phenomena and cultural features. His analysis further demonstrated that this model can account for two unusually large concentrations of sites and motifs: the Coso Range petroglyphs and the Carrizo Plain pictographs. But unanswered was the question of whether the model is applicable more widely, especially to smaller sites and localities made by different cultural groups. We documented and analyzed three petroglyph localities with seven small petroglyph sites in the southern Mojave Desert, California, to test this model. These sites are attributed to the Takic-speaking Cahuilla and Serrano tribes. Our study revealed a good fit between the expected natural and cultural variables associated with rock art site locations, with the number of such variables present at any given locale potentially correlated with the size of the individual sites. In addition to the research value of these results, this suggests that the model may be useful in the predictive modeling of rock art site locations for heritage management purposes.

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.

Supporting construction cost breakdown analysis with an ontological modeling technique

ABSTRACT Accurate cost estimation is imperative to implementing a successful construction project. Cost breakdown analysis (CA) is one of the most essential tasks of estimating a construction cost accurately, and this task relies heavily on the experience and knowledge of cost estimators. However, in practice, CA is not effectively performed because of limited time for implementing an analysis, lack of required knowledge by cost estimators, and unorganized knowledge of cost estimators. Studies have also rarely discussed construction cost estimation from the perspective of CA. This paper thus proposes a novel framework that includes representation and reasoning functionalities for addressing these practical and research limitations. The proposed framework is based on ontological modeling that enables organizing CA knowledge systematically, connecting it semantically, and retrieving it inferentially. A case study was performed for validating the framework. The validation results demonstrate that the proposed framework advances conventional CA by efficiently and effectively identifying essential information about CA items and their unit prices, generating CA sheet templates, and ensuring that all necessary CA items are accounted for. Its representation and reasoning capabilities can enhance the organization and retrieval of cost estimators’ knowledge, facilitate cost estimation, and thus overcome limitations present in traditional CA processes.

Pretending the city: Cinema as urban planning tool

Rose's connection of the City Digital Twin and its visual logics and ideologies to the disaster film is an illuminating one, but is articulated through the intensive usage of CGI, formal and stylistic strategies, and narrative and thematic content closely affiliated with the disaster film and other subtypes of the action film genre. If we move beyond the confines of a generic exploration and instead approach the CDT through an ontological model of cinema and cinematic-descended screen media in general, other facets of not only the CDT, but also the city's relationship with image and representation, emerge.

Deformation mechanism and damage energy evolution of coal body under different gas pressures based on the energy principle

With increasing mining depth, the coal pillars of a coal mine will be in a stressful environment characterized by high gas pressures and unidirectional loading. To investigate the damage evolution characteristics and energy evolution mechanism of coal pillars loaded in a gas pressure environment, a uniaxial compression test was performed on a coal body under different gas pressures using a load testing apparatus for gas-containing coal rocks. The obtained results showed that the mechanical properties of the coal body varied with the gas pressure. Specifically, the peak strain, compressive strength, and elastic modulus decreased with increasing gas pressure; the higher the gas pressure, the lower the conversion rate of the elastic strain energy in the elastic deformation stage of the coal body and the lower its total input energy. With increasing gas pressure, the damage threshold of the coal body decreased, whereas the damage variable corresponding to the peak value, as well as the damage threshold value, increased. According to the theory of continuous damage mechanics, an ontological damage model of the coal body under different gas pressures was established based on the principle of minimum energy dissipation, and the rationality of the model was verified through a comparison between the theoretical and experimental data. Our findings can be useful in ensuring the safety of coal mining in terms of preventing gas disasters.

Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management

The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs.

Two senses of representation in science

Accounts of scientific representation typically assume that there is a single sense of “represent”, and they attempt to develop a theory that can account for all its features. The aim of this article is to draw the consequences of a distinction between two senses of “represent” that has been proposed recently. Taking inspiration from the distinction between speaker-meaning and expression-meaning in philosophy of language, a first sense is analysed in terms of the mental states of the user of a vehicle in context, and a second sense in terms of communal norms constraining contextual uses. I argue that making this distinction, and thus understanding the representation relation as essentially indexical and normative, can help us move beyond the controversies between various accounts of scientific representation, notably what have been dubbed informational and functional accounts, as well as debates regarding the ontology of scientific models.

Methods for semantic analysis of the state of the process of functioning of a system for detecting, preventing and eliminating the consequences of computer attacks

Objective. The aim of the study is to determine the most effective method of semantic analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks. Method. The study was conducted based on the methods of semantic analysis of the state of the process of SOPKA operation. Result. A structural model of the system of semantic analysis of the state of the process of SOPKA operation is proposed, which is capable of providing an analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks. The most effective method for solving the problem of semantic analysis of the state of the process of SOPKA operation is machine learning using ontological modeling. Conclusion. The results indicate the need for further research of the system of semantic analysis of the state of the process of detection, prevention and elimination of consequences of computer attacks.

A knowledge graph-based intelligent planning method for remanufacturing processes of used parts

Intelligent remanufacturing process planning is crucial for the efficient and high-quality remanufacturing of used parts with complex failure characteristics. However, due to the varied failure characteristics of used parts, the diversity of remanufacturing processes, and complex non-linear relationships among remanufacturing process elements, relying solely on mathematical programming or manual empirical is difficult to effectively model and optimise the remanufacturing process planning. To this end, a knowledge graph-based intelligent planning method for remanufacturing processes is proposed to enhance efficiency and quality by combining mathematical programming and knowledge reuse. Firstly, with failure characteristics as decision nodes, a full-element remanufacturing process ontology model is constructed, linking used parts, failure characteristics, and corresponding process plans. The BERT-BiLSTM-CRF model extracts remanufacturing process entities, and a remanufacturing process knowledge graph (RPKG) is constructed. Secondly, an intelligent decision-making model based on graph multi-node path retrieval is proposed. Aim to minimise carbon emissions, time, and cost, combining feature similarity calculations and nearest neighbour search (NNS) to efficiently retrieve the optimal process plan for each failure characteristic. Then, the optimal process plans are merged based on process constraints to create the complete plan. Finally, a concrete case is given to verify the effectiveness and advantages of this method.

Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework

PurposeEffective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems.Design/methodology/approachThis study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs.FindingsThe final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally.Practical implicationsThe proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research.Originality/valueAcademic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking.

Virtual simulators in the automated control system subsystem for training operators of metal-cutting machines

The article describes the proposed method of automated construction of virtual simulators for the subsystem of personnel training of the automated control systems of production. Its distinctive feature is the work with the customer based on the provision of functional videos from production, as well as the formalization of the design and development process in the form of structures for the process of creating software and visual modules taking into account the specifics of the development of virtual reality simulators. An ontological model has been developed for the construction of virtual simulators for training machine tool operators in the automated control systems of production, which uses the identified basic concepts and relationships between them in the subject area of work of machine tool operators. Its implementation is based on the Protege software environment. The following diagrams are proposed: functional and contextual IDEF0 of the functioning of virtual simulators for training in working on universal metal-cutting machines: milling and turning. The architecture of a virtual simulator for use in the subsystem of training personnel of automated control systems of production, as well as a structural diagram of the formation of practice-oriented skills in working on metal-cutting machines using virtual simulators are described.