Ontology Model Research Articles

Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

Unsafe behavior identification on construction sites by combining computer vision and knowledge graph–based reasoning

PurposeWorkers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.Design/methodology/approachThis study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.FindingsA case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.Originality/valueExisting studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

A Study on a Spatiotemporal Entity-Based Event Data Model

An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly important for describing the real world. By analyzing the representational categories of the existing event data models, the representation of existing event models was found to have different emphases and not be sufficiently balanced, and the universality and comprehensiveness need to be improved. Therefore, based on the advantages of the ontological event model in expressing semantic information and the advantages of the object-event-based spatiotemporal data model in expressing entity multidimensional characteristics and dynamic processes, a spatiotemporal entity-based event data model and the modeling method were designed to provide model support for event organization and processing. Additionally, the Long March and its important battles were selected as case studies to validate the proposed model. The validation shows that the proposed model performs well in terms of event dynamics, hierarchical structure, and complex interrelationships.

Design and Development Model of a Web Accessibility Ecosystem

The article examines issues of web accessibility ecosystems for people with special needs. Methods, models, accessibility standards, and technologies related to the structure, design, and functionality of the web accessibility ecosystem are studied. The stages of developing an accessibility ecosystem are explored. The accessibility of the design, functionalities, structure, and content of a particular ecosystem are presented. Several themes for the design of the system with an emphasis on its accessibility for blind users are explored and analyzed. UX/UI design and the ontological model of accessibility, used in the implementation of the model of the ecosystem and its elements, are studied. A web accessibility ecosystem model has been developed, compliant with the Web Content Accessibility Guidelines and based on semantic technologies. Other qualities of this model are easy access to information resources on the topic of accessibility, convenience for users with different needs, and the possibility of expansion and enrichment in the future.

An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection.

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.

OACTD: an ontology-embedded model for heritage tourism reviews to assign cultural tourism data

ABSTRACT The rapid growth of cultural tourism, driven by the ‘heritage boom’, has brought about the urgent need to reduce the cost and broaden the audience of cultural tourism. To address this challenge, this paper proposes OACTD, an ontology-embedded model for heritage tourism reviews to assign cultural tourism data. The OACTD model integrates scattered and unstructured tourist reviews, making them directly beneficial to cultural tourists and related management organizations, while also reducing the costs of tourism. The model first integrates large volumes of tourist reviews from various popular tourism platforms, and then utilizes natural language processing (NLP) algorithms to extract key information. This information is subsequently structured and visualized through an ontology model, which is further enhanced based on tourist queries. To demonstrate the effectiveness of the model, a case study was conducted on the Xiangjiang Ancient Town Group, involving the extraction of key information and the construction of an ontology from 8016 tourist reviews. This study presents a novel approach to leveraging cultural tourism data, promoting the formation of a two-way ‘demand-supply’ digital cultural tourism ecological chain.

A Method for Constructing an Urban Waterlogging Emergency Knowledge Graph Based on Spatiotemporal Processes

Urban waterlogging is one of the major “diseases” faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban waterlogging over time. Therefore, the objective of this study is to analyze the time, events, properties, geographic objects, and activities associated with urban waterlogging emergency responses from the geographic spatial and temporal processes perspective and to construct an urban waterlogging emergency knowledge graph by combining top-down and bottom-up approaches. We propose a conceptual model of urban waterlogging emergency response ontology based on spatiotemporal processes by analyzing the basic laws and influencing factors of urban waterlogging occurrence and development. Secondly, we describe the construction process of the urban waterlogging emergency response knowledge graph from knowledge extraction, knowledge fusion, and knowledge storage. Finally, the knowledge graph was visualized using 159 urban waterlogging events in China from 2020–2022, with a quality assessment indicating 81% correctness, 65.5% completeness, and 95% data conciseness. The results show that this method can effectively express the spatiotemporal process of an urban waterlogging emergency response and can provide a reference for the spatiotemporal modeling of the knowledge graph.

An adaptable Digital Twin model for manufacturing

In a smart manufacturing system, physical assets are often connected to their Digital Twins in the virtual world that composes a cyber-physical production system (CPPS). Digital Twin is synchronized with the physical asset. Digital Twin is therefore considered an enabling technology of smart manufacturing. In the process of developing Digital Twins, the Digital Twin model is of great significance. Many Digital Twin models have been proposed in the past decades, including geometry models, information models, and ontology models. However, there has not been an adaptable and generic Digital Twin model that can be used for various scenarios in a manufacturing system. This paper proposes an adaptable Digital Twin model based on the object-oriented concept. The proposed Digital Twin model can be applied to different situations using object-oriented concept features. Firstly, the base Digital Twin model is developed according to ISO 23247, including the essential attributes of a Digital Twin. The aggregation and composition mechanism of the proposed Digital Twin model is also described based on the object-oriented concept. After that, according to the categories of the physical assets on the shop floor, Digital Twin models of different physical assets are developed based on the base Digital Twin model, inheriting the features of the base Digital Twin model. In the end, a case study is carried out for verification on a machine tool in the Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS).

Ontological analysis of business process modeling of higher education institutions based on the electronic document management system

Abstract Improving the quality of the educational process, bringing it to modern requirements is a complex task, the solution of which requires a systematic approach from higher education institutions and the development of a strategy to improve the quality of service and efficiency of the institution. Formalization and modeling of the management business process allows for effective management decisions. When analyzing large amounts of information to effectively solve the problem of data structuring, as well as the problem of forming new knowledge, it is proposed to use an ontological approach based on the use of associative rules. The ontological analysis of modeling business processes of higher education institutions and analysis of the structure, functions and information flows of organization and management of educational activities based on the structural approach is carried out. Improving the quality of the educational process, bringing it to modern requirements is a complex task, the solution of which requires a systematic approach from higher education institutions and the development of a strategy to improve the quality of service and efficiency of the institution. The use of an ontological model of interaction between subjects and objects of the system makes it possible to assess the level of system efficiency and build a simulation model of management, which will allow predicting the emergence of critical business processes that may lead to a decrease in the efficiency of decision-making and disruptions in the functioning of higher education institutions and their departments.

Intertwining Narrative and Technology: Reinterpreting European Cultural Heritage through the Voices of Latin American Women Writers

Travel literature not only provides relevant information about cultural heritage, such as authorship, chronology or location, but also reveals the impressions evoked by its reception. This article describes the methodology of the ongoing REWIND project, which explores European cultural heritage through the perspectives of Latin American women writers who travelled to Europe in the early twentieth century. The project seeks to amplify the voices of non-European women committed to feminism and sociocultural diversity to foster decolonial, inclusive and pluralistic historical narratives. On the one hand, we address the configuration of the project corpus, the semantic annotation process, and the design of the ontological modelling according to the deep data concept. On the other hand, we explain the analysis of the relationships between cultural heritage, time, space and emotion. The project uses travel literature in digital format to retrieve, study and interpret data on cultural heritage, creating a workflow that integrates information from various historical sources to raise further questions and provide new insights. The proposed methodology follows the FAIR principles and prioritizes using open-source software to ensure reproducibility and facilitate the use of deliverables in other studies.

Towards a Proto Artificial General Intelligence: The Role of Large Language Model Ontologies in its Development

Towards a Proto Artificial General Intelligence: The Role of Large Language Model Ontologies in its Development

ANALYSIS OF APPROACHES TO DESIGN ONTOLOGICAL MODELS OF AN ADAPTIVE SOFTWARE SYSTEM

The paper analyzes approaches to the design of ontological models of adaptive software systems. To evaluate the quality of the projected ontologies, specialized groups of metrics are used, which allow us to determine the shortcomings in the structure and functioning of the model. An analysis of the use of ontological models in the process of designing and developing adaptive software systems has been carried out. It was established that the ontological approach allows for a more effective presentation of the subject area, which improves the software configuration process. The use of ontological models allows flexible responses to changes in user requirements without the need to directly update the source code of the software. The process of designing ontologies based on classical and abstract approaches is presented. It was determined that the use of a classical approach to the design of ontological models could complicate the adaptation process due to the need to change the structure and reconfiguration of the system. To solve this problem, an abstract approach is proposed that ensures the dynamism of the adaptation process, without the need for a complete reconfiguration of the system. A set of specialized metrics is defined that allows for the analysis of the ontological model of the software system, the evaluation of its syntactic and semantic quality, compliance with rules and interoperability, and the possibility of expansion. A comparative analysis of abstract and classical approaches to the design of ontological models was carried out based on defined structural metrics, as well as metrics of the scheme and filling of the knowledge base. The obtained values of structural and hierarchical metrics confirm the quality and effectiveness of the proposed approaches. According to the defined metrics, the absence of cycles and entanglement in the ontological model indicates the correctness of the construction of the structure from the point of view of ergonomics and the possibility of further changing the structure and filling of the ontological model. The analyzed values of the schema metrics and the filling of the knowledge base demonstrate the better effectiveness of the abstract approach in the process of determining the software configuration, which provides faster and more correct processing of ontological SWRL rules during adaptation.

The Sharing Model of Kindergarten Art Enlightenment Education Curriculum Resources Based on Deep Learning Algorithm

In order to meet the autonomous needs of children’s art learning, this paper studies the sharing model of kindergarten art enlightenment education curriculum resources under the deep learning algorithm. Build a kindergarten art enlightenment education curriculum resource-sharing model that includes the classification and storage layer of education curriculum resources, the metadata annotation layer of education curriculum resources and the shared business service layer. The deep learning algorithm is used in the classification and storage layer of education curriculum resources. The education curriculum resources are divided into courseware resources, case resources and media materials resources through unsupervised restricted Boltzmann machine (RBM) coding and supervised training process. The ontology model of various educational curriculum resources is constructed, and the semantic information of educational curriculum resources is described using ontology. The metadata annotation layer of education curriculum resources annotates the metadata semantics of education curriculum resources through the constructed ontology, which improves the consistency and sharing of education curriculum resources management. The implementation of resource sharing business service is based on semantic analysis and query of educational curriculum resources. Therefore, the shared business service layer adopts a semantic query method based on improved similarity algorithm, which combines semantic distance, node density, node depth and relationship type to build a semantic similarity calculation method to achieve semantic analysis and query of educational curriculum resources. The experimental results show that the model is suitable for multi-classification of shared resources in the kindergarten art enlightenment education curriculum, and the semantic query accuracy is high in the process of sharing. When the threshold is [Formula: see text] 0.5, the query effect of the sharing model in this paper is ideal, which can meet the needs of users’ autonomous learning.

Entity and relation extractions for threat intelligence knowledge graphs

Advanced persistent threats (APTs) represent a complex challenge in cybersecurity as they infiltrate networks stealthily to conduct espionage, steal data, and maintain a long-term presence. To combat these threats, security professionals increasingly rely on cyber knowledge graphs (CKGs), which provide scalable solutions to analyze and structure vast amounts of cyber threat intelligence (CTI) from diverse sources in real-time, enabling the automation of proactive security measures. Developing CKGs requires extracting entity and their relationships from unstructured CTI reports. However, existing approaches face significant limitations, such as difficulties with the nuances of cybersecurity language, diverse threat terminologies, and high rates of error propagation, resulting in low accuracy and poor generalizability. This paper introduces a novel Threat Intelligence Knowledge Graph (TiKG) pipeline designed to address these challenges. The TiKG framework leverages SecureBERT, a domain-specific transformer-based model optimized for cybersecurity, and integrates it with an attention-based BiLSTM to capture the context and nuances of security texts, reducing error propagation and improving extraction accuracy. Additionally, the pipeline incorporates a domain-specific ontology and inference model to ensure precise relation mapping in relation extraction. Using three large-scale TI open-source datasets (DNRTI, STUCCO, and CYNER) and a curated CTI dataset, extensive evaluations demonstrate the effectiveness of our framework, showing significant improvements over existing methods in detecting and linking cyber threats. These contributions provide a robust platform for security professionals to analyze and predict potential attacks, develop effective defenses, and enhance the strategic capabilities of cybersecurity operations.

The Art of Soft Systems Inquiry: Retracing the Footsteps of Churchman and Checkland

AbstractIn this paper I reflect upon soft-systems inquiry. To do this I revisit Churchman’s discourse on the philosophical ideas underpinning inquiring systems and then discuss the practical application of soft inquiry pioneered by Checkland. This body of work suggested ontological models of organisational behavior were inappropriate and only by adopting an epistemological perspective would provide the basis for understanding complex systems that characterise human society. These ideas motivated a new way of addressing complexity. This work created a difficulty for action research since to be faithful to this discovery meant that any method of inquiry should be one that could cope with such complexity. One outcome was Soft Systems Methodology (SSM). Paradoxically the success of SSM has restrained further discussion of these ideas. The purpose of this paper is to revisit the ideas of Churchman and Checkland as a means of returning to first principles. In this paper I reassess their ideas and those of Husserl and Gadamer and suggest there is more to be gained from this rich seam of systems thinking.

Research on the Management Model of University Teaching Materials based on Knowledge Graph

Colleges and universities will produce a large number of teaching materials in their teaching work. It is of great significance to collect and manage teaching materials. In this study, knowledge graph technology is applied to the management of teaching materials in colleges and universities, and an ontology model of teaching materials in colleges and universities based on knowledge graph is proposed. Based on the relevant norms of teaching resources ontology in China, the ontology model of teaching materials in colleges and universities is constructed by seven-step method, and it is transformed into the attribute graph model of teaching materials in colleges and universities, which is stored in the Neo4 j graph database. The model can define the entity type and the relationship between entities, and construct the subject knowledge framework.

Method for Developing the System Architecture of Existing Industrial Objects for Digital Representation Tasks

This paper presents a method for creating the system architecture of existing industrial objects based on Model-Based Systems Engineering (MBSE) principles. The method aims to form a digital representation of physical objects, which is crucial in the digital transformation of industrial enterprises. It allows for the accurate reflection of all components, processes, functions, and interrelationships within an object. The methodology includes stages of data collection, structuring, development of ontological models, and the integration of a comprehensive system architecture into the digital space. This method was tested using a small hydroelectric power plant, revealing its key advantages and disadvantages and identifying areas for further improvement. The main findings indicate a significant improvement in understanding the system architecture for scenario modeling and digital operation of the objects. Despite challenges such as the need for multiple iterations and high data requirements, the methodology demonstrates the potential for applying MBSE in the digital transformation of existing industrial objects.

Fuzzy deep visual panic detection

Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users’ emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user’s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users’ stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users’ profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user’s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.

Organization of Secure Data Routing in Electric Power Complexes Using Ontological and Deep Models

Organization of Secure Data Routing in Electric Power Complexes Using Ontological and Deep Models

The Method of Evaluation for Complex Systems and Processes Based on Ontological and Neuro-Fuzzy Modeling

The Method of Evaluation for Complex Systems and Processes Based on Ontological and Neuro-Fuzzy Modeling