Implementation Of Ontology Research Articles

Design and Implementation of Time Metrology Vocabulary Ontology

Design and Implementation of Time Metrology Vocabulary Ontology

A unified framework for digital twin development in manufacturing

The concept of digital twin (DT) is undergoing rapid transformation and attracting increased attention across industries. It is recognised as an innovative technology offering real-time monitoring, simulation, optimisation, accurate forecasting and bi-directional feedback between physical and digital objects. Despite extensive academic and industrial research, DT has not yet been properly understood and implemented by many industries, due to challenges identified during its development. Existing literature shows that there is a lack of a unified framework to build DT, a lack of standardisation in the development, and challenges related to coherent goals of DT in a multi-disciplinary team engaged in the design, development and implementation of DT to a larger scale system. To address these challenges, this study introduces a unified framework for DT development, emphasising reusability and scalability. The framework harmonises existing DT frameworks by unifying concepts and process development. It facilitates the integration of heterogeneous data types and ensures a continuous flow of information among data sources, simulation models and visualisation platforms. Scalability is achieved through ontology implementation, while employing an agent-based approach, it monitors physical asset performance, automatically detects faults, checks repair status and offers operators feedback on asset demand, availability and health conditions. The effectiveness of the proposed DT framework is validated through its application to a real-world case study involving five interconnected air compressors located at the Connected Facility at Devonport Royal Dockyard, UK. The DT automatically and remotely monitors the performance and health status of compressors, providing guidance to humans on fault repair. This guidance dynamically adapts based on feedback from the DT. Analyses of the results demonstrate that the proposed DT increases the facility’s operation availability and enhances decision-making by promptly and accurately detecting faults.

Towards the implementation of functional ontologies for federal government ministries

Towards the implementation of functional ontologies for federal government ministries

Towards the Implementation of Functional Ontologies for Federal Government Ministries

Towards the Implementation of Functional Ontologies for Federal Government Ministries

Enhancing Learning Personalization in Educational Environments through Ontology-Based Knowledge Representation

In the digital age, the personalization of learning has become a critical priority in education. This article delves into the cutting-edge of educational innovation by exploring the essential role of ontology-based knowledge representation in transforming the educational experience. This research stands out for its significant and distinctive contribution to improving the personalization of learning. For this, concrete examples of use cases are presented in various academic fields, from formal education to corporate training and online learning. It is identified how ontologies capture and organize knowledge semantically, allowing the intelligent adaptation of content, the inference of activity and resource recommendations, and the creation of highly personalized learning paths. In this context, the novelty lies in the innovative approach to designing educational ontologies, which exhaustively considers different use cases and academic scenarios. Additionally, we delve deeper into the design decisions that support the effectiveness and usefulness of these ontologies for effective learning personalization. Through practical examples, it is illustrated how the implementation of ontologies transforms education, offering richer educational experiences adapted to students’ individual needs. This research represents a valuable contribution to personalized education and knowledge management in contemporary educational environments. The novelty of this work lies in its ability to redefine and improve the personalization of learning in a constantly evolving digital world.

Ontological Method for the Modeling and Management of Building Component Construction Process Information

Knowledge of the construction process plays a decisive role in guiding construction and thus affects the quality of buildings. However, the integration and relevance of information in the traditional process file are poor, and it is easy to ignore the process changes caused by the differences when applying information directly. Therefore, this paper introduces an ontological method for the information modeling and management of the construction process. The proposed method uses machine readable language to integrate process knowledge in a structured way, thereby improving the relevance of information and promoting the reuse, sharing and retrieval of knowledge. At the same time, the semantic web rule language (SWRL) rules are used to model the relevant laws and regulations with reference to the construction product quality acceptance regulations and to correlate the construction process information so as to ensure the compliance of building products and the accuracy of process information. Finally, the feasibility of the method is verified via specific use cases on an ontology implementation platform.

COMPATIBILITY PROVISIONING OF SENSOR SYSTEMS AND INDUSTRIAL INTERNET OF THINGS PLATFORMS

The article relates to the issue of ensuring the compatibility of different sensors types, their measurements results and the industrial Internet of things platforms. The solution offered to ensure semantic interoperability is based on semantic technology with the architecture development and further creation of a common ontological model. The approach offered allows integrating both semantic content of sensor information and methods for arrangement and normalization of measurement results, which is relevant for subsequent analysis and use of industrial Internet of things data on various platforms, including data depersonalization for computer-assisted learning. A study of regulatory documentation and technical solutions to ensure the compatibility of the industrial Internet and sensor systems was carried out, existing similar solutions were considered, shortcomings in comparison with the proposed method were identified. Ontology concepts for the model of semantic compatibility of sensor systems and the industrial Internet of things platform were developed. The article gives an example of the developed logical inference procedure in the framework of the ontology implementation with use of the free software tool Protégé. Using the proposed approach allows to proceed to knowledge-based interoperability.

An ontology-based KBE application for supply chain sustainability assessment

Product Lifecycle Management (PLM) plays a key role in digital transformation demanded by Industry 4.0 and life cycle assessment, including sustainability assessment. Knowledge Based Engineering (KBE) applications can support PLM by integrating heterogeneous knowledge from different stages throughout the product life. However, the integration of knowledge from different stages and teams can cause misunderstanding if not represented in a unified form. Furthermore, different forms of knowledge used by different software are neither machine-readable nor human-readable, which also sets obstacles to knowledge integration in KBE applications. Supply chain sustainability assessment is such a scenario that entails integrating knowledge from different sources. This paper firstly implements a sustainability assessment method from other scholar to calculate the supply chain sustainability performance and adapts a sustainability assessment ontology for supply chain sustainability assessment. Then, an example KBE application is developed by implementing the sustainability assessment ontology and calculation method to simulate the knowledge sharing and integration between different teams. Finally, through this example application, it is discussed that the implementation of ontology to represent knowledge in PLM application for collaborative tasks like sustainability assessment can increase the efficiency of data sharing and integration. This paper is a proof of concept for the ontology-based framework. This framework can facilitate to represent knowledge but not create new knowledge, which means it can increase the efficiency of the software development, but can not provide a better calculation method and assessment framework for supply chain sustainability assessment. • An ontology-based application simulates the knowledge sharing and integration in sustainability assessment. • Ontology-based knowledge representation provides a unified and flexible data format. • Ontology-based knowledge representation makes a unified knowledge base possible. • Ontology increases the efficiency of data sharing and integration in collaborative tasks. • High-efficient software development contributes to supply chain sustainability management.

Implementation of Zebrafish Ontologies for Toxicology Screening.

Toxicological evaluation of chemicals using early-life stage zebrafish (Danio rerio) involves the observation and recording of altered phenotypes. Substantial variability has been observed among researchers in phenotypes reported from similar studies, as well as a lack of consistent data annotation, indicating a need for both terminological and data harmonization. When examined from a data science perspective, many of these apparent differences can be parsed into the same or similar endpoints whose measurements differ only in time, methodology, or nomenclature. Ontological knowledge structures can be leveraged to integrate diverse data sets across terminologies, scales, and modalities. Building on this premise, the National Toxicology Program’s Systematic Evaluation of the Application of Zebrafish in Toxicology undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency. To accomplish this, zebrafish researchers were asked to assess images of zebrafish larvae for morphological malformations in two surveys. In the first survey, researchers were asked to annotate observed malformations using their own terminology. In the second survey, researchers were asked to annotate the images from a list of terms and definitions from the Zebrafish Phenotype Ontology. Analysis of the results suggested that the use of ontology terms increased consistency and decreased ambiguity, but a larger study is needed to confirm. We conclude that utilizing a common data standard will not only reduce the heterogeneity of reported terms but increases agreement and repeatability between different laboratories. Thus, we advocate for the development of a zebrafish phenotype atlas to help laboratories create interoperable, computable data.

Semantic Conceptual Framework for Environmental Monitoring and Surveillance—A Case Study on Forest Fire Video Monitoring and Surveillance

This paper presents a semantic conceptual framework and definition of environmental monitoring and surveillance and demonstrates an ontology implementation of the framework. This framework is defined in a mathematical formulation and is built upon and focused on the notation of observation systems. This formulation is utilized in the analysis of the observation system. Three taxonomies are presented, namely, the taxonomy of (1) the sampling method, (2) the value format and (3) the functionality. The definition of concepts and their relationships in the conceptual framework clarifies the task of querying for information related to the state of the environment or conditions related to specific events. This framework aims to make the observation system more queryable and therefore more interactive for users or other systems. Using the proposed semantic conceptual framework, we derive definitions of the distinguished tasks of monitoring and surveillance. Monitoring is focused on the continuous assessment of an environment state and surveillance is focused on the collection of all data relevant for specific events. The proposed mathematical formulation is implemented in the format of the computer readable ontology. The presented ontology provides a general framework for the semantic retrieval of relevant environmental information. For the implementation of the proposed framework, we present a description of the Intelligent Forest Fire Video Monitoring and Surveillance system in Croatia. We present the implementation of the tasks of monitoring and surveillance in the application domain of forest fire management.

Industrial ontologies for interoperability in agile and resilient manufacturing

Ontologies provide an opportunity to tackle the interoperability challenge in digital manufacturing. Although ontologies have been used in numerous industrial projects, the problem of semantic interoperability in the industry is still unresolved. One reason for the lack of success of ontologies in the industry is that most industrial ontologies have been developed based on local needs without taking wider applications into account or attempting to learn from earlier ontology initiatives or reuse the existing ontologies. In this position paper, we argue that for widespread adoption and implementation of ontologies, more systematic and coordinated efforts are needed with the participation of industry, academia, and government at an international level. We also hypothesise that a coordinated and systematic ontology development effort will incrementally lead to the creation of an ecosystem of interoperable software applications that support consistent data access and reasoning across the product life cycle. We discuss why efforts such as Industrial Ontology Foundry (IOF) can promote a methodological ontology development process by introducing modular, and reusable ontologies. We draw examples from two domain areas, namely supply chain management and production planning and scheduling, to demonstrate how industrial notions can be analysed ontologically according to IOF procedures and methods.

Towards metrics-driven ontology engineering

The software engineering field is continuously making an effort to improve the effectiveness of the software development process. This improvement is performed by developing quantitative measures that can be used to enhance the quality of software products and to more accurately describe, better understand and manage the software development life cycle. Even if the ontology engineering field is constantly adopting practices from software engineering, it has not yet reached a state in which metrics are an integral part of ontology engineering processes and support making evidence-based decisions over the process and its outputs. Up to now, ontology metrics are mainly focused on the ontology implementation and do not take into account the development process or other artefacts that can help assessing the quality of the ontology, e.g. its requirements. This work envisions the need for a metrics-driven ontology engineering process and, as a first step, presents a set of metrics for ontology engineering which are obtained from artefacts generated during the ontology development process and from the process itself. The approach is validated by measuring the ontology engineering process carried out in a research project and by showing how the proposed metrics can be used to improve the efficiency of the process by making predictions, such as the effort needed to implement an ontology, or assessments, such as the coverage of the ontology according to its requirements.

Description and Implementation of Production Factory Ontology

The article follows the previous work of the authors. The paper aims to elaborate ontology into a solid form using directly for the production factory. The authors divided the article into two parts. The first part explains the basics of conceptualization, and the second part is devoted to creating and describing ontology using the RDF model. As an example of ontology’s practical use, there are codes in the programming language #C.

The concept of just war in Russian and American linguistic cultures

The study examines the concept of just war in the military-political discourse and technologies of manipulating the mass consciousness used by the leaders of the Russian and American states in speeches based on the idea of just war. Lexical and semantic analysis of the lexemes “just” and “war” in Russian and English is carried out. The relevance of the study is determined by the increasing attention to the military-political discourse and actualization of the concept. It received consistent substantiation and development in the works of medieval scholastics. In the 21st century, it serves the purposes of moral and ethical justification of modern military conflicts. The purpose of the study was to analyze the concept of just war at the lexical and semantic level and describe its ontological foundations and implementation in the Russian and American mental lexicons. Based on the analysis, the authors have identified general and particular aspects of the just war concept in the Russian and American mental lexicons and described the mechanisms of mind control in the political discourses of B. Obama and V.V. Putin.

Open Text Ontology Mining to Improve Retrievals of Information

Information retrieval is the main task to extract relevant information from documents. Mostly, the information retrieval system is based on the keyword approach to extract the knowledge of relevant documents. The experiment shows the ontology can improve the result to overcome the weakness of keyword approach. Ontology implementation method is based on phrase formation and semantic relationships between words. This study tested 10 Malay documents using ontology to retrieve information. The results obtained were compared with the result obtained from manual information retrieval done by experts for precision and recall measure. In this study, there are three semantic relationships between words that are capable of expressing knowledge in documents. They are taxonomy relationship, attribute relationship and non-taxonomy relationship. The relationship of ontology can be formed by using taxonomy relationships algorithm, attribute relationships algorithm and non-taxonomy relationships algorithm based on the linguistic rules of the Malay language. The result of precision and recall for this experiment shows that the ontology approach can enhance the performance of information retrieval from the relevant documents.

ІНФОРМАЦІЙНА СИСТЕМА СЕМАНТИЧНОЇ ІДЕНТИФІКАЦІЇ І ДОКУМЕНТУВАННЯ РЕЗУЛЬТАТІВ НЕФОРМАЛЬНОГО Й ІНФОРМАЛЬНОГО НАВЧАННЯ: МЕТОДИ Й ІНСТРУМЕНТИ РОЗРОБКИ

У статті розглядається актуальна проблема – розроблення інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання, яка спрямована на забезпечення ефективної взаємодії ринку праці з ринком освітніх послуг. Проаналізовано стан сучасного ринку праці та економіки, а також необхідність упровадження інформаційних е-засобів для ефективного управління, обробки і використання інформації щодо визнання результатів неформального й інформального навчання, підвищення «видимості» та цінності результатів навчання, які отримані поза системами формального навчання. У роботі розглянуто методи та проаналізовано інструменти розроблення інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання. Зокрема обґрунтовано доцільність використання семантичних технології для обробки даних Semantic Web, головним завданням яких є оброблення інформації на рівні знання; запропоновано в якості основи інформаційної системи використання європейського класифікатора ESCO; модель предметної області описано на основі онтологічних баз знань. У публікації проаналізовано інструменти розробки інформаційної системи, здійснено концептуальне проєктування та розроблення пропонованої системи. Визначено основні можливості та користувачів інформаційної системи, описано основні етапи створення інформаційної системи: створена онтологічна схема; описано процес інтеграції онтології в RDF-сховище; наведені приклади взаємодії з RDF-сховище шляхом застосування мови запитів SPARQL та конекторів GraphD; розроблено архітектуру та інтерфейс користувача запропонованої інформаційної системи. Результати роботи SPARQL-запитів до даних представлені у вигляді наборів результатів. У якості програмних інструментів для розробки вебсервера для надання користувачам доступу до RDF-сховища прийнято мову РНР та PHP фреймворк Laravel. Дослідження обґрунтовують використання бібліотеки React для розробки інтерфейсу користувача інформаційної системи. У публікації визначено, що подальшим напрямком розвитку дослідження може бути імплементація ідей, методів і самої онтології в інші додатки для практичного використання всіма учасниками ринку праці.

Феномен рефлексии в контексте онтологического рассмотрения

The peculiarity of the holistic-ontological approach is revealed in the article. Reflection is viewed as a modal object, as a process that takes place in the general dynamics of Being. The significance of the analysis of the diversity of the structural components of reflection as a whole, establishing the features of their interaction, as well as the influence of the modus-forming beginning of reflection on human being. The connection of the problem of reflection with the ontology of human life and the phenomenon of existential accomplishment is shown. The specificity of modes of reflection is considered as a transition from ontic to ontological levels of being. Moreover, the specificity of inauthentic and authentic manifestation of reflection is highlighted. It is concluded that the ontological status and nature of reflection changes depending on the specifics of its manifestation at the fundamental levels of human existence. Reflection actualizes the transcending mechanism, pushing the boundaries of the ontological implementation of the personality.

An ontological artifact for classifying social media: Text mining analysis for financial data

In this paper we utilize a structured natural language processing implementation of the Financial Industry Business Ontology (FIBO) to extract financial information from the unstructured textual data of the social media platform Twitter regarding financial and budget information in the public sector, namely the two public-private agencies of the Port Authority of NY and NJ (PANYNJ), and the NY Metropolitan Transportation Agency (MTA). This research initiative uses the Design Science Research (DSR) perspective to develop an artifact to classify tweets as being either relevant to financial bonds or not. We apply a frame and slot approach from the artificial intelligence and natural language processing literature to operationalize this artifact. FIBO provides standards for defining the facts, terms, and relationships associated with financial concepts. We show that FIBO grammar can be used to mine semantic meaning from unstructured textual data and that it provides a nuanced representation of structured financial data. With this artifact, social media such as Twitter may be accessed for the knowledge that its text contains about financial concepts using the FIBO ontology. This process is anticipated to be of interest to bond issuers, regulators, analysts, investors, and academics. It may also be extended towards other financial domains such as securities, derivatives, commodities, and banking that relate to FIBO ontologies, as well as more generally to develop a structured knowledge representation of unstructured data through the application of an ontology.

Context-oriented ontology in food safety management

Actuality of the usage of the food safety knowledge-based technologies is shown. The food safety stakeholders and information objects are presented. The set of ontologies and context areas which are described decision –making tasks and processes are shown. The basic ontology is presented as a means of conceptual representation of the field of food safety. The usage of decision-making is considered. Modern food processing technologies, food safety requirements, food safety requirements (agricultural production, food processing, food logistics, food sales, food storage) etc. are characterized by the need for complex and rational solutions. It is necessary to consider different aspects of decision-making in food safety, which can be based not only on the knowledge of a particular subject area. The use of information and knowledge is a key task in any problem area related to the production and dissemination of knowledge, especially new types of unique (and valuable) content that are useful and accessible. The paper aim is to conceptually present an ontological system based on the use of context and ontologies and aimed at supporting decision making in the food safety management system. Food safety knowledge, which used in decision-making processes, is considered as the context knowledge and the knowledge that describes the content. The paper presents food safety stakeholders and provides information objects. The information and knowledge includes various aspects such as knowledge and information on the production and processing industry; and the food safety aspects. For this purpose, an interconnected set of ontologies is used, which is a multilevel associative structure: meta-ontology; basic ontology; context ontology; set of domain ontologies; realization ontology; user presentation and interaction ontology; model of inference machine. Implementation of food safety processes and integration of the components of decision-making is based on the presentation of the multi-level system of management and decision-making in it through a model of a context. The context framework takes into account different context domains, such as: purpose/result, actor, process/action, object, environment, facility, tools, presentation, location, and time. The ontology implementation builds an inter connected system of preparation and choice of solutions, both for the given problem and for interaction with other complexes of problems and tasks, allows to make decisions taking into account the consequences of their implementation.

Mapping Crisp Structural Semantic Similarity Measures to Fuzzy Context: A Generic Approach

Ontology-based similarity measures have received much importance in recent years. In many real-world cases, the domain considered in the ontological similarity assessment consists of uncertainty or incomplete information. Such vagueness has led to the successful implementation of fuzzy ontology (FO)-based similarity measures. Despite various applications of FO-based similarity measures, limited methods have so far been proposed for this purpose. Accordingly, this paper presents a generic model for semantic similarity assessment based on a fuzzy ontology. The proposed approach relies on the broad literature of Crisp Ontology-based Structural Semantic Similarity Measures (CO-SSSM). It provides an approach for mapping CO-SSSMs to fuzzy context. Consequently, the proposed generic model can be applied to various CO-SSSMs to develop their corresponding FO-SSSMs. In this regard, as an empirical investigation, four of the common CO-SSSMs were selected, their equivalent FO-SSSMs were developed by means of the proposed approach, and the accuracy of their similarity assessment was compared with each other. The results show the power of FO-SSSMs in describing the relations between concepts and their superiority over CO-SSSMs.