Semantic Ontology Research Articles

Towards ML Models’ Recommendations

AbstractArtificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Model-based safety analysis of requirement specifications

Model-based design primarily aims to establish a communication framework throughout a system’s design. Moreover, models with formal semantics allow verification based on rigorous methods, including the analysis of system safety. However, building formal models is a tedious manual process and cannot be easily applied to real problems.A key gap that hinders automation of model development is that there is no systematic way to connect system requirements with the activity of model-based design. In this article, we introduce a workflow to tackle this gap and ultimately automate the analysis of system safety using formal methods.We extend our previous work on boilerplate-based specification of system requirements with ontological semantics towards specifying FDIR (Failure, Detection, Isolation, Recovery) requirements. The workflow is centered around the automated generation of a model skeleton in SLIM, a component-based formal modeling language, from a set of ontology-based requirement specifications. Our approach has been implemented into a dedicated tool, which not only provides visualization of the ontology relations, but also supports traceability of the analysis findings back to the requirements specification. Finally, we provide results on the safety analysis of a real star-tracker system based on a SLIM model derived by minimally changing the auto-generated model skeleton.

A Hybrid Approach to Ontology Construction for the Badini Kurdish Language

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology.

Revolutionizing semantic integration of maintenance cost prediction for building systems using artificial neural networks

The maintenance cost management for building services has significantly advanced with the advent of computational techniques and semantic web technologies. Building services, including HVAC, plumbing, firefighting, and electrical systems, ensure building safety and efficiency. However, predicting maintenance costs is challenging due to the complexity and variability in usage patterns and environmental conditions. To address this challenge, this study introduces a Semantically Integrated Knowledge-based Artificial Neural Network (SIKB-ANN) framework. The SIKB-ANN framework integrates Semantic Web Technology (SWT) and Artificial Neural Networks (ANN) to improve the accuracy of maintenance cost predictions. SWT enhances data interoperability and standardization, while ANN manages complex non-linear data relationships. The model was trained with historical maintenance data from a firm (2017–2022) and validated using metrics such as Coefficient of Determination (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The model achieved high predictive accuracy, with R2 values of 0.986 for the test set and 0.944 for the training set, indicating its robustness and reliability. An essential contribution of this research is the innovative integration of semantic ontologies with ANN, significantly enhancing predictive capabilities and providing a structured data management framework. This approach improves prediction accuracy and supports better decision-making and resource allocation in building management. The study highlights the potential of combining semantic technologies with machine learning to address complex predictive challenges in the built environment. Future research should consider integrating real-time data streams, advanced machine learning techniques, and broader applications in facility management.

Research on the construction and mapping model of knowledge organization system driven by standards

With the rapid development of artificial intelligence and enterprise digital transformation, the standardization organization, storage and management of semantic knowledge in computers have become the current research focus. As the core theory of knowledge system construction, knowledge organization (KO) provides theoretical support for the study of semantic knowledge organization and representation, among which knowledge organization system (KOS) is the important tool of semantic organization. At present, many scholars have carried out research from different perspectives of KOS based on theory, which provides the direction for the sustainable development of KOS. However, most of these studies focus on some aspects of KOS, which are in a "scattered" state, lacking systematic analysis of the basic principles of KOS construction and semantic organization based on theories and international standards. Therefore, this paper firstly constructs KOS theoretical models in the conceptual world and computer world respectively through a comprehensive study of multi-disciplinary basic theories such as semantics, logic, system theory, and international standards such as ISO 1087:2019, ISO 25964:2013, and ISO 11179:2023, and traces the iterative construction, organization and mapping process from "concept" in the conceptual world to "metadata" knowledge and semantics in the computer world. The semantic organization based on metadata is realized in computer. Secondly, on this basis, in order to realize ontology representation of domain knowledge, the ontology construction method based on MDR metadata is proposed. Finally, taking the semantic organization and ontology construction of Epicentre model in petroleum field as an example, the feasibility of the ideas and methods proposed in this paper is verified. The model and method proposed in this paper is independent of the specific type of KOS, so it is innovative and universal. The methodology is also applicable to other fields of conceptual system modeling, metadata standard construction, and data model modeling.

Developing A Semantic Ontology to Represent Knowledge About Thai Amulets

This study aims to construct a comprehensive ontology for Thai amulets that accurately encapsulates the scope of knowledge, structure, and concepts pertaining to this subject. The ontology development process encompasses three primary stages. The initial stage involves gathering expertise from specialists and relevant literature on Thai amulets. The subsequent stage examines individuals interested in amulets' behavior and information requirements. The final stage entails the creation of the ontology using the Protege program. This process is subdivided into three main tasks: 1) defining the objectives of the ontology, 2) constructing the ontology, and 3) evaluating the ontology. The development of the amulet knowledge ontology resulted in the establishment of 17 main classes and 20 subclasses. Structural efficiency was assessed using the OntOlogies Pitfall Scanner (OOPS!). Understanding the structural integrity of the amulets facilitates their dissemination and community utilization. Expert evaluations indicate that the ontology achieved a comprehensive efficiency rating of 4.63. Additionally, creating a semantic search system is deemed suitable for further development.

Integrated representation of technical systems with BIM and linked data: TUBES system ontology

Technical systems in the Architecture, Engineering, Construction, and Operations (AECO) industry are complex interconnected structures. To cope with this high complexity, large amounts of data must be processed and exchanged throughout the whole life cycle of these systems. Building Information Modeling (BIM) and Semantic Web Technologies (SWT) offer the possibility to structure and link this data as well as to define common terminology. Semantic Web ontologies describing HVAC systems in the AECO industry have been developed, but an ontology for an integrated representation based on general system theory is missing. Therefore, this study proposes the TUBES System Ontology (TSO) as a knowledge representation for an integrated, explicit representation of linked technical systems, their hierarchical subdivision, structural and functional interconnections, and relationships to spatial entities. The contribution is validated on two example models, and the results are critically discussed, pointing out corresponding limitations as well as the need for further research.

Design and Implementation of an Ontology for Measurement Terminology in Digital Calibration Certificates.

Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford's Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization.

Semantic Web Usage in the Tourism Industry in Andalusia, Spain

Andalusia got the 4th place of tourists’ favorite destinations among 17 autonomous communities in Spain in September 2023 and almost returned to pre-pandemic normality regarding tourism. Although Spain is famous for its “sun and beach”, its tourist industry is facing new challenges in socio-economic, environmental and territorial issues; therefore, the role of artificial intelligence cannot be ignored in the evolution and transformation of the tourism business. AI algorithms reinforce decision-making capabilities through analysing real-time data. Effectiveness and efficiency of AI algorithms are dependent on well-structured, well-described and machine-readable meta data network which can be provided by Semantic Web standards. This article empirically study the current usage of Semantic Web formats, ontologies and touristic topics in 65 tourist-relevant web sites in Andalusia. Results show that just 57 % of examined sites used semantic markup with most of the semantic annotations (98.5%) made in three formats: Microdata, JSON-LD and RDFa. Furthermore, the Ontologies used in almost 90% of markups are ‘Schema.org’, ‘Data Vocabulary’ and ‘RDF’, but none of the special tourism ontologies appeared in examined tourism web sites. Therefore, Andalusia tourism websites should be enhanced with much more widespread Semantic Web in order to achieve a better performance in a future in which AI technologies will be widespread.

Developing a data repository to support interdisciplinary research into childhood stunting: a UKRI GCRF Action Against Stunting Hub protocol paper

IntroductionAs a topic of inquiry in its own right, data management for interdisciplinary research projects is in its infancy. Key issues include the inability of researchers to effectively query diverse...

Retraction: GerardDeepak, ArumugamSanthanavijayan. QGMS: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence. Comput Intell40: e12514, 2024 (10.1111/coin.12514)

The above article, published online on 08 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest‐edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. The authors have been informed of the decision to retract.

Research on the Historical Remain of the Bombard of Chongqing Ontological Model from the Perspective of Digital Humanities

Based on the perspective of digital humanities, taking the example of the historical remain of the bombard of Chongqing, this research explores the possibilities of digital communication of the historical remain of the bombard of Chongqing through specific methodssuch as multi-source clustering, resource indexing, knowledge reorganization, and interactive operations. The semantic ontology model can achieve a multidimensional stereoscopic description of the historical remain of the bombard of Chongqing with heterogeneous data from multiple sources. This facilitates the in-depth integration of related resources of relic objects, designing an ontology model that aligns with the unique characteristics of Chongqing's red culture. The study also discusses the technical and expansion paths of the ontology model for war relics, aiming to provide insights and references for the integration of digital resources and the digital preservation and dissemination of the historical remain of the bombard. To achieve the preservation of Chinese historical and cultural heritage.

A taxonomy for cybersecurity standards

Cybersecurity standards on a global scale are exhaustive, appealing to several types, such as glossaries, guidelines, methods, and objectives (e.g., Information Technology evaluation, requirement identification, risk management, and technical specifications). This chaotic range of standards towards the rapid pace of technological and threat evolution hinders stakeholders (e.g., security architects/developers, policymakers, testers, and auditors) from discovering which standards best meet their security needs. The paper analyzes this challenge and contributes to harmonizing standards by identifying relationships between the EU regulation and prominent cybersecurity standards. The current work develops a taxonomy that classifies cybersecurity standards according to their objective, usage, and sector, aiming to help stakeholders understand their purpose and decide which they should adopt to cover their organizational needs. The taxonomy is represented in a semantic ontology, following the Web Ontology Language Edition 2 knowledge engineering approach. A realistic scenario is described to illustrate the applicability of the taxonomy.

Developing an Ontology Representing Fall Risk Management Domain Knowledge

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults.

Exploring the Landscape of Smart Cities: A Comprehensive Review of IoT and Cyber-Physical Systems

They focus on housing, well-being, equality, clean energy and fair conditions. The cyber-physical approach involves the development of IoT and Cyber-Things. Smart cities have a variety of use cases, including electricity and transportation. Automating is used for efficiency in industrial manufacturing. An integrated supply and demand side management system is required for the reliability, security and ability to manage the power grid. This paper introduces an integrated energy approach, enhances existing standards, and establishes a shared basis for multidisciplinary planning. It also introduces new semantic network ontologies to provide a comprehensive framework for solving resource-related challenges. This new approach aims to fill the gaps in current standards and create an integrated environment for multi-stakeholder collaboration, using a semantic web ontology for communication and improved decision making in energy systems Provides information integrated, including various forms of smart cities With flexibility for flexibility and inclusion in the energy industry, can accommodate the specific characteristics and needs of various smart city applications In this study, computing -physical system (CPS), software-defined network (SDN), internet (IoT). ), and analyze how smart cities are connected. CPS combines physical channels with electronic systems to provide increased network management efficiency and flexibility. SDN improves dynamic capacity and flexibility, while IoT is more connected for real-time data exchange and decision-making.

Ep-Satty-MCDM based business decision-making model using behaviour and review data

Reaching the maximum number of customers at the right time to increase sales and profitability of the business is the primary goal of Electronic Commerce (E-Commerce). However, owing to the low-influenced product, the profitability of e-commerce has been drastically affected in recent years. Therefore, this work proposes an Empirical Probability assigned Satty’s method integrated Multicriteria Decision-Making model (EP-Satty-MCDM)-based business decision-making model for improving the sales of low-influenced products by advertising with celebrities. Primarily, the authenticated user securely downloaded the encrypted data using Armstrong number private key generated-Trident Curve-Cryptography (Arm-TCC) in the web application. After that, the data is cleansed and the attributes of review and behavior data are extracted. Then, by utilizing the Interval-valued Atanassov intuitionistic fuzzy-based Mann-Whitney U test (IAF-MWU), the correlation between the review and behavior data is evaluated. The correlated features under each user are mapped under the product, and semantic ontology is constructed, where the data is again mapped with the product’s subsections. Afterward, the domains are extracted. Thereafter, to identify whether the product is high-influenced or low-influenced, the obtained ratings from ontology and extracted domains are inputted into the Boosting Regression Tree-Recurrent Neural Network (BRT-RNN). Then, for the decision-making, the positively forecasted celebrities with their garment and low-influenced products are given as the input to EP-Satty-MCDM. The experimental outcomes exhibited that the proposed technique withstands maximum accuracy when contrasted with the existing methodologies.

Exploring the Prospects and Challenges of Artificial Intelligence in Shaping the Future of Web 3.0

Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.

Reality as Technology, or Ontological Lexicon in Modern Processes Nomination

The study examines terminological nominations in the IT sphere in the Russian, which include lexicon denoting ontological concepts. The aim of the research is to identify a conceptual shift in the perception of reality, as explicit in new terminological nominations of the digital era (augmented reality, mixed reality, RR). Composite nominations with key nouns of ontological semantics were extracted from scientific and media discourse. Research methodology includes semantic and contextual analysis, interpretative analysis within the framework of the theory of linguistic conceptualization of the world. The following conclusions were drawn: the use of words of ontological semantics in IT technical terms reflects a technocentric view of objective reality, where its fundamentally significant role in human and societal organization is reimagined. The concept of “reality” is juxtaposed with the concept of “virtuality.” The “real reality” itself, designated as ‘RR’, is considered one of the components in the paradigm of immersive technologies. Semantic changes are evident in the adjective ‘realnyy’ [real]: in opposition to attributes like ‘virtualnyy’ [virtual], ‘onlayn’ [online], etc., it embodies a new semantic nuance ‘manifesting not in virtual space’, which becomes relevant in discussions about the role of digital technologies in life.

A state-of-the-art universal machine learning framework for decoding suspect coded messages

This proposed framework aims to mitigate global terrorist threats, suspicious activities, and legal violations by introducing an innovative system that monitors encrypted short-text messages across instant messaging programs. In the current landscape, criminals, terrorists, and clandestine entities extensively employ encrypted messages for illicit endeavours on a global scale. Despite the escalating use of encrypted messages on social media platforms, a comprehensive solution for combating online crime is notably absent.The outlined framework addresses this gap by employing a cryptographic system that integrates semantic web ontology, a Predefined Dataset, and machine learning technology for encryption, decryption, and cryptanalysis. A key feature is the framework's ability to predict potential crimes by analyzing blogs for suspicious terminology conveyed through coded information. The identified information is then promptly communicated to law enforcement agencies, thereby alleviating the burden on numerous security entities and offering an efficient strategy against global crime.What sets this proposed framework apart is its unique utilization of encrypted text, a dimension that has remained unexplored in previous studies. The cohesive integration of its components expedites the process of anticipating crime types and significantly reduces the incidence of encrypted crimes on a global scale.