Ontology Model Research Articles

Structural models of forming an integrated information and educational system “quality management of higher and postgraduate education”

The study examines the design of an information and educational System for quality education management. The requirements for the information environment of universities are formulated. The delineation of distinct stages in the technological process for shaping the values of information objects is outlined, achieved through the execution of information procedures. These procedures generate a technologically comprehensive product applicable within the university context. A set of heterogeneous systems for automating university activities makes obtaining integral characteristics based on information from different sources difficult. The research suggests an approach that allows you to get rid of the disadvantage. The introduction of an integrated information environment has been reviewed. To implement the information system “quality Management of Higher and Postgraduate Education,” a conceptual model of the system architecture and a logical structure was developed. The developed information model was based on the model of a complex information and educational environment of a higher educational institution and the Ontological model of the database of the integrated information and educational environment. The preliminary results of the pilot implementation of the system in the activities of Astana IT University are summarized, which made it possible to improve the quality management of the educational process.

FOG COMPUTING TECHNOLOGY IN DISTRIBUTED SYSTEMS

Topicality. The concept of fog computing is an evolutionary stage in the development of the cloud concept. It occupies a leading position among the general trends in the development of information technology. The emergence of this concept is closely related to the origin and development of the concept of the Internet of Things. The results. The subject area was analyzed. It includes an analysis of current trends in the field of organizing distributed computing, an analysis of the use of population algorithms and ontology models for solving optimization problems in distributed systems, an analysis of models, methods and algorithms for solving the problem of transferring the computational load in distributed systems implemented on the basis of fog computing. Conclusion. It has been revealed that the concept of fog computing makes it possible to solve most of the problems associated with the load on the communication infrastructure and the latency of information exchange. But they do not resolve issues related to the high dynamism of the foggy environment and the concomitant decrease in the efficiency of the distributed system.

Information extraction from automotive reports for ontology population

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.

Toward an ontology for EA modeling and EA model quality

Models have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.

Applications of digital twins in medicine and the ontological model of medical digital twins

The article demonstrates the applications, essence and possibilities of digital twin technology and explains the advantages of digital twin technology and potential challenges. A digital twin is a digital copy that can be used to simulate the status of a physical object or system. The integration of digital twin technologies with the Internet of things, Big Data and Artificial intelligence offers innovative solutions for quicker detection of arisen problems (or problems to arise) related to changes in a real physical object and for making relevant decisions. The development of such solutions in the healthcare is crucial for preserving human health and providing people with higher-quality medical care. It is possible to get an early diagnosis of the disease and the selection of a more effective treatment method on the produced digital twin by transferring the patient’s physical characteristics and changes in his/her body to the digital environment. The article analyzes medical digital twins, categorizing their benefits into patient health, cost reduction, self-management, and other benefits. In order to ensure adaptability and effectiveness in disease therapy and healthcare management decision-making, the patientoriented ontology of healthcare is examined, and a four-level ontological model is suggested to create its digital twin. The creation of a patient-oriented digital twin of healthcare requires the creation of a digital twin of existing physical objects at each level of its ontological model. The creation of digital twin in healthcare opens wide opportunities for making decisions on provision of safe and high-quality medical care to patients.

A Distributed Dynamic Framework to Allocate Collaborative Tasks Based on Capability Matching in Heterogeneous Multirobot Systems

Collaboration among a group of robots with heterogeneous capabilities is an important research problem that enables to combine different robot functionalities, and thus, conducts complex tasks that may be difficult to achieve by a single robot with limited resources. In this paper, we propose a new distributed task allocation framework based on the capability matching of heterogeneous robots. The framework is composed of an ontological dynamic knowledge graph model and a hardware control scheme to model the capability and optimize resource utilization for collaborative tasks. We introduce an intuitive hardware control scheme based on a dynamic knowledge graph that resolves possible conflicts between the hardware control of different types of robots. Action sequences are produced by a task and motion planning algorithm to collaboratively perform the assigned task. The performance of the proposed methodology is evaluated by both simulations and hardware experiments.

Developing an Appendicectomy Surgical Pathway Ontology (ASPO).

This study proposes an ontology model to represent the surgical pathways for appendicectomy, incorporating domain knowledge extracted from literature and electronic records in an Australian health district. The ontology comprised 108 concepts and 81 object properties. The model is useful for continuous quality improvement initiatives, i.e., process mining to understand what happens in the hospital versus what is required by the policy.

Ontological Modeling and Clustering Techniques for Service Allocation on the Edge: A Comprehensive Framework

Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be performed. They have specific computational or other types of requirements and must also be executed at a particular physical location. Moreover, both services and devices may change dynamically. In this context, methods are needed to effectively schedule efficient allocations of services to computational resources. In this article, we present a framework to address this problem. Our proposal first uses knowledge graphs for describing software requirements and the availability of resources for services and computing nodes, respectively. To this end, we proposed an ontology that extends our previous work. Then, we proposed a hierarchical filtering approach to decide the best allocation of services to computational nodes. We carried out simulations to evaluate four different clustering strategies. The results showed different performances in terms of the number of allocated services and node overload.

Real-life data-driven model predictive control for building energy systems comparing different machine learning models

By considering forecasts and exploiting storage effects, model predictive control can achieve significant energy and cost savings in the building sector. However, due to the high individual modeling effort, model predictive control lacks practical applicability. For that reason, data-driven process models, approximating the system behavior based on measurements, have become increasingly popular in recent years. Still, scientific literature lacks consent about the most promising model types and efficient workflows to integrate different machine learning models into a model predictive controller. With this work, we present a workflow to provide efficient model predictive controllers based on measurement data automatically. The main idea is to translate different machine learning models into optimization syntax to enable efficient optimization with full access to gradients. We currently consider artificial neural networks, gaussian process regression, and simple linear regression process models. We use a generic model ontology to automatize the controller generation further and test the methodology on two real-life use cases. The first use case is the application of five office rooms with smart thermostat valves. The second use case is a test hall with an air handling unit and a concrete core activation. Using only two days of initial training data, we deploy controllers based on the different model types for six weeks in the offices and apply online learning to improve the models continuously. We observe only minor differences in controller performance despite the artificial neural networks showing the highest prediction accuracy. The second use case shows that the simple linear models require less controller tuning effort. Thus, for practical applications, we recommend linear regression models.

Commonsense reasoning and automatic generation of IoT contextual knowledge: An Answer Set Programming approach

The increasing interest in searching for intelligent, proactive, and autonomous environments leads to the necessity of accessing contextual knowledge: knowledge about changes that occur in the environment and even about the capabilities of the devices that compose a specific deployment. This knowledge would allow carrying out commonsense reasoning (exhibit human-like comprehension) according to the current situation, enabling decision making and task planning. One of the most used forms of knowledge specification is ontologies, but ontological knowledge modeling is usually a manual process, making it a costly task. In addition, ontologies can be found that have been designed for specifying knowledge about IoT-related concepts, but many are overly complex or do not have an adequate orientation that allows modeling the system’s capabilities. There are many different types of reasoners, but Answer Set Solvers are of particular interest for commonsense reasoning. This paper proposes an ontology to represent contextual knowledge about IoT device capabilities, and an Answer Set Programming-based reasoner for the automatic generation of this knowledge. The main challenge is to demonstrate that contextual knowledge can be generated through commonsense reasoning processes implemented with Answer Set Programming, and that the resulting knowledge can be used for decision making (also through commonsense reasoning). This work shows how the generated knowledge is correct through different use cases, presents an application example that demonstrates the benefits of using the generated knowledge, and analyzes the reasoner performance to demonstrate that the execution time is adequate.

DEVELOPMENT OF APPLIED ONTOLOGY FOR THE ANALYSIS OF DIGITAL CRIMINAL CRIME

Context. A feature of the modern digital world is that crime is often committed thanks to the latest computer technologies, and the work of law enforcement agencies faces a number of complex challenges in the digital environment. The development of information technology and Internet communications creates new opportunities for criminals who use digital traces and evidence to commit crimes, which complicates the process of identifying and tracking them.
 Objective. Development of an applied ontology for a system for analyzing a digital criminal offense, which will effectively analyze, process and interpret a large amount of digital data. It will help to cope with the complex task of processing digital data, and will also help automate the process of discovering new knowledge.
 Methods. To build an ontological model as a means of reflecting knowledge about digital crime, information was collected on existing international and domestic classifications. The needs and requirements that must be satisfied by the developed ontology were also analyzed. The creation of an ontological model that reflects the basic concepts, relationships in the field of digital criminal offense was carried out in accordance with a recognized ontological analysis of a specialized subject area.
 Results. An applied ontology contains the definition of entities, properties, classes, subclasses, etc., as well as the creation of semantic relationships between them. At the center of the semantics is the Digital Crime class, the problem area of which is complemented by the interrelated classes Intruder, Digital evidence, Types of Crime, and Criminal liability. Such characteristics as motive, type of crime, method of commission, classification signs of digital traces and types of crime, as well as other individual information were assigned to the attributes of the corresponding classes. An ontological model was implemented in OWL using the Protégé software tool. A feature of the implementation of the applied ontology was the creation of SWRL rules for automatically filling in additional links between a class instance. Manual and automatic verification of the ontology showed the integrity, consistency, a high degree of correctness and adequacy of the model. The bugs found were usually related to technical aspects and semantic inconsistencies, which were corrected during further development iterations.
 Conclusions. The research confirmed the effectiveness of the developed applied ontology for the analysis of digital criminality, providing more accurate and faster results compared to traditional approaches.

Tools for ranking objects of the mineral resource complex based on ontology

Mineral resources form the basis for the development of the vast majority of industries. At the same time, most of them are non-renewable. To determine promising exploration activities in conditions of limited human, financial, and sometimes technical resources, it is necessary to take into account a large number of factors when setting exploration tasks for the reproduction of mineral resources. To solve problems with relatively weak formalization, it is customary to use the mathematical apparatus of fuzzy logic in decision support systems. However, it must be based on an ontological model that will contain the interrelationships of the elements of the system and allow you to find implicit relationships between them. In this paper, based on the mathematical apparatus of fuzzy logic and the Mamdani method, it is proposed to form an ontology of geological and economic monitoring in relation to a mineral resource facility (MRF), an industrial raw materials hub (IRMH) and an administrative entity. As a result, using the Reasoner component built into Protege, indicators of geological and economic monitoring, taxonomic units and the frequency of information collection for designing a decision support system in the geological industry were determined. It is possible to create such a convergence, taking into account the heterogeneity of data storage systems, on the basis of an ontological model. The advantage of using ontology is the high level of this tool flexibility, taking the form of heterogeneous data integration within a single storage system. This article attempts to use ontological models framework together with artificial intelligence for the geological subject area in terms of solid minerals, particularly common ones, and ground waters.

From Text to Action: NLP Techniques for Washing Machine Manual Processing

This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals.

Integrated Interaction Journey and Privacy Risk Assessment: A Graph Model

Smart airports involve several applications and stakeholders to facilitate passenger journey. Passengers interact with those stakeholders and share their personal information using the smart airport applications. While the use of smart airport applications offers several benefits, it also puts passengers’ personal information at risk. This draws our attention to the need for identifying and understanding privacy risks with a view to protect passenger information at smart airports. Our earlier systematic literature review study revealed a gap in modelling passenger information privacy risks in the context of smart airports. This paper aims to address this gap by developing an ontology for interaction journey and privacy risk assessment modeling. The contribution of the proposed ontology is to bring new knowledge and understanding of privacy risks in the contemporary smart airport context. The development and evaluation of the proposed framework follows the Design Science Research (DSR) method along with the ontology development techniques. The proposed ontology aims to assist privacy experts in modelling and analyzing privacy risks relevant to passenger information in the smart airport context.

An Ontological Behavioral Modeling Approach With SHACL, SPARQL, and RDF Applied to Smart Grids

An Ontological Behavioral Modeling Approach With SHACL, SPARQL, and RDF Applied to Smart Grids

Устойчивость и безопасность зеленых финансов с позиции многоагентных игр

Green finance increases the innovation potential of green transformation, helps to address climate change, the environmental crisis and energy security. It is important for green finance to achieve sustainable and balanced development, which will increase the security of both finance itself and many processes in the real economy that depend on it. The purpose of this study was to develop approaches to the ontological design of green finance and multi-agent modeling of the behavior of green finance actors, considering their sustainable and safe interaction. To achieve this goal it was necessary to clarify the conceptual apparatus of green finance and related areas; to characterize the "work" of the multi-agent model in the analysis of the behavior of the main green finance actors and to trace the prospects for their impact on the stability and security of the financial market; and finally, based on ontology construction and agent-based approach to nonlinear interactions in the green financial process, to determine the mechanism of building chains of the main market participants. The methodological basis of the study consists of general scientific research methods (analysis, synthesis, induction, deduction, logic), as well as evolutionary dynamics and agent-based approach to the analysis of financial markets. A comparative analysis of the works of various authors and the interpretation of theoretical provisions in related areas of knowledge about finance, as well as the analysis of their sustainable and safe development were carried out. As a result of the study, the authors identified the main approaches to the design of financial ontology and multi-agent modeling of financial interactions in the direction of determining their sustainable and safe development. The proposed model establishes that the criterion of the effectiveness of the applied strategy is the satisfaction with the state of financial impact on the economy, environment, and social sphere. Maintaining this state, along with saving the amount of resources used, is done through optimization. The chosen approach corresponds to the concept of sustainable development and safe functioning of the systems under consideration.

Advancements in Artificial Intelligence and Data Science: Models, Applications, and Challenges

The text that follows gives an overview of the numerous uses of artificial intelligence (AI) and data science in a variety of fields, focusing on their potential advantages and drawbacks. Healthcare, finance, education, smart cities, and the development of disease therapies have all showed promise for AI. The study examines several AI models, such as ontological, statistical, hybrid, and biological models, in addition to the restrictions placed by deep learning techniques. The study on brain architecture and function is also included in the article, along with applications of multivariate pattern analysis, bioinspired systems, and machine learning to the analysis and processing of enormous datasets. The literature study emphasizes how critical it is to address societal and ethical issues with AI.

Business ecosystem formation ontological mechanism

The goal of the work is to develop a scientifically based ontological mechanism for the formation of business ecosystems, the terminology of which has emerged as a result of the manifested basic principles of digital transformation of the economy, science and education. It is shown that these principles make it possible to determine the mutual influence of many more factors in the planning and management of business, including scientific and educational. In other words, there was an order to study both classical agricultural ecosystems and the transfer of natural ecosystem mechanisms to the functioning of economic systems, which, together with the possibility of integrating environmental factors and taking into account their interrelationships in a digital economy, led to the emergence of such research areas in the field of ecosystems as business ecosystems. The above principles have led to the development of mechanisms for the integration of information resources not only within one industry, but also in most related industries, for which ontological modeling of their subject spaces should be carried out, the conceptual description of which has become significantly different over the years. Research in this area has been carried out using agriculture as an example, since it is one of the most powerful ecosystems in the classical sense and is closely related to a large number of other industries.

Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data

Abstract This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.

ОНЕЙРИЧЕСКИЙ СИМВОЛИЗМ И МУЛЬТИПОЛЯРНАЯ СУБЪЕКТИВНОСТЬ В ФЕНОМЕНОЛОГИЧЕСКОЙ ОНТОЛОГИИ М. МЕРЛО-ПОНТИ

In the lecture course of 1954–1955, dealing with the issue of “passivity” in phenomenological philosophy, M. Merleau-Ponty pays much attention to the “unconscious” as “Freud’s discovery”. The philosopher finds approaches to the unconscious by analysing the phenomena of dreaming, memory, etc. on the basis of Freud’s descriptions and offering his own terminology and interpretations of psychoanalytic observations. Merleau-Ponty sets out to overcome the limitations of the original readings of psychoanalytic discovery due, among other things, to the reliance of Freud’s theoretical models on the classical Cartesian concept of subjectivity. In this article, we will consider the theory of “oneiric symbolism”: an area of “mythical” sense-formation, which very phenomenal existence – at the boundary between unconscious and consciousness, activity and passivity, subject and world – pushes Merleau-Ponty to revise classical ontological models. We will show for what fundamental issues of phenomenological ontology Merleau-Ponty uses the concept of “oneiric symbolism” and what conclusions he manages to reach with its help, and briefly characterise the mutual relation between the models of subjectivity offered by phenomenological ontology and Freudian psychoanalysis.