Search Results (572)

This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clustering approach, we map the evolution of semantic technologies, revealing core research themes such as ontology engineering, knowledge graphs, and linked data. Furthermore, we address existing research gaps, including challenges in the semantic web, dynamic ontology updates, and scalability in Big Data environments. By synthesizing insights from the literature, our research provides an overview of the current state of semantic web research and its prospects. With a 0.75 coherence score and perplexity = 48, the topic modeling analysis identifies three distinct thematic clusters: (1) Ontology-Driven Knowledge Representation and Intelligent Systems, which focuses on the use of ontologies for AI integration, machine interpretability, and structured knowledge representation; (2) Bioinformatics, Gene Expression and Biological Data Analysis, highlighting the role of ontologies and semantic frameworks in biomedical research, particularly in gene expression, protein interactions and biological network modeling; and (3) Advanced Bioinformatics, Systems Biology and Ethical-Legal Implications, addressing the intersection of biological data sciences with ethical, legal and regulatory challenges in emerging technologies. The clusters derived from BERT embeddings and clustering show thematic overlap with the LDA-derived topics but with some notable differences in emphasis and granularity. Our contributions extend beyond theoretical discussions, offering practical implications for enhancing data accessibility, semantic search, and automated knowledge discovery. Full article

We investigate the impact of textual content and its structural characteristics on the detection of false information. We propose a Bidirectional Graph Convolutional Neural Network (ICP-BGCN) that integrates message content with its propagation paths for enhanced detection performance. Our approach leverages web propagation topology by transforming disconnected user posts into a bidirectional propagation graph, which integrates top-down and bottom-up pathways derived from post forwarding and commenting relationships. Using BERT embeddings, we extract contextual semantic features from both source texts and their propagated counterparts, which are embedded as node attributes within the propagation graph. The bidirectional graph convolutional neural network subsequently learns the feature representations of the event propagation network during information dissemination, merging these representations with the original text content features to achieve comprehensive disinformation detection. Experimental results demonstrate significant improvements over existing methods. On benchmark datasets Twitter15 and Twitter16, our model achieves accuracy rates of 89.7% and 91.7%, respectively, outperforming state-of-the-art baselines by 1.1% and 3.7%. The proposed ICP-BGCN exhibits strong cross-domain generalization, attaining 84.4% accuracy on the Pheme dataset and achieving improvements of 1.8% in accuracy and 3.8% in Macro-F1 score on SemEval-2017 Task 8. Full article

Verifying sequential consistency (SC) in concurrent programs is computationally challenging due to the exponential growth of possible interleavings among read and write operations. Many of these interleavings produce identical outcomes, rendering exhaustive verification approaches inefficient and computationally expensive, especially as thread counts increase. To mitigate this challenge, this study introduces a novel approach that efficiently verifies SC by identifying a minimal subset of valid event orderings. The proposed method iteratively focuses on ordering write events and evaluates their compatibility with SC conditions, including program order, read-from (rf) relations, and SC semantics, thereby significantly reducing redundant computations. Corresponding read events are subsequently integrated according to program order once the validity of write events has been confirmed, enabling rapid identification of violations to SC criteria. Three algorithmic variants of this approach were developed and empirically evaluated. The final variant exhibited superior performance, achieving substantial improvements in execution time—ranging from 31.919% to 99.992%—compared to the optimal existing practical SC verification algorithms. Additionally, comparative experiments demonstrated that the proposed approach consistently outperforms other state-of-the-art methods in both efficiency and scalability. Full article

Cross-site scripting attacks represent one of the major security threats facing web applications, with Stored XSS attacks becoming the predominant form. Compared to reflected XSS, stored XSS attack payloads exhibit temporal and spatial asynchrony between injection and execution, rendering traditional browserside defenses based on request–response differential analysis ineffective. This paper presents XSShield, the first detection framework that leverages a Large Language Model to understand JavaScript semantics to defend against Stored XSS attacks. Through a Prompt Optimizer based on gradient descent and UCB-R selection algorithms, and a Data Adaptor based on program dependence graphs, the framework achieves real-time and fine-grained code processing. Experimental evaluation shows that XSShield achieves 93% accuracy and an F1 score of 0.9266 on the GPT-4 model, improving accuracy by an average of 88.8% compared to existing solutions. The processing time, excluding model communication overhead, averages only 0.205 s, demonstrating practical deployability without significantly impacting user experience. Full article

Background/Objectives: During the last decade, there has been an increased interest in phenolic compound-rich natural products as natural therapies for regulating the molecular pathways behind central obesity and associated metabolic disorders. The present scoping review presents the outcomes of clinical and preclinical studies examining the anti-obesity effects of high phenolic extra virgin olive oil (HP-EVOO) and its possible underlying molecular mechanisms. Methods: Studies published between 2014 and 2024 were searched via MEDLINE, Scopus, Cochrane, the Web of Science, Semantic Scholar, Google Scholar, Science.gov, and Clinicaltrials.gov databases. A combination of keywords and Boolean logic was used to search throughout the last decade in all databases, including “hyperglycemia” or “hypertension” or “metabolic syndrome” or “dyslipidemia” or “hyperlipidemia” or “hypoglycemia” or “obesity” or “macrovascular diabetic complications” or “microvascular diabetic complications” or “cardiovascular disease” or “overweight” or “insulin sensitivity” or “insulin resistance” and “extra virgin olive oil” or “high phenolic olive oil” and “human” or “animal model”. Results: The 10-year literature survey identified 21 studies in both animal models and humans, indicating that HP-EVOO improves inflammation, glycemic control, oxidative stress and endothelial function, potentially protecting against metabolic syndrome, hypertension and type 2 diabetes, even compared to EVOO. Moreover, HP-EVOO’s antiplatelet effect and improvement in HDL functionality reduce cardiovascular risk. Conclusions: The evidence presented in this study demonstrates that HP-EVOO represents an effective preventive and therapeutic dietary approach to cardiometabolic diseases. Full article

Product semantics is essential for car styling because it shapes how consumers perceive and interact with cars, influences user experiences, and allows for product differentiation. Although many AI tools are available to assist car designers, research on applying machine learning techniques to evaluate product semantics is rare. Therefore, we developed a classification model that helps designers identify and evaluate the semantics conveyed by car styling using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool. We used Python web scraping to collect isometric drawings and introductory articles of 1320 SUV cars of various brands from 2009 to 2024 via websites such as Car Body Design and Car Design News. We also summarized four semantic types of car styling, namely “aggressive”, “sporty”, “clean”, and “off-road”, to create the dataset. We used WEKA image classification to randomly select 792 (60%) images from the dataset to train a classification model of car styling semantics. The remaining 528 images (40%) were used for verification. The classification model trained with the Binary Pattern Pyramid Filter and the Random Forest classifier achieved an accuracy of 84.6%. The model was evaluated in terms of whether 10 SUVs created by 10 graduate design students using AI conveyed the anticipated product semantics. Seven of the ten SUVs were correctly classified and the rest were not. All of the participants agreed that the predictions were satisfactory. However, it is necessary to improve the accuracy of each semantic classification, especially the “clean” type. The results of this study demonstrate the capability of machine learning to identify the semantics of car styling effectively, improve the communication and evaluation of product semantics by designers in the design process, and create a car styling with a good appearance that resonates with consumers. Full article

To address the challenges surrounding information sharing and low efficiency during the engineering quality risk management process, this paper constructs a digital process for engineering quality risk management. Engineering quality risk factors were identified through literature analysis and synthesis, and relevant standards, specifications, and project information were collected to construct an engineering quality risk information ontology. The Semantic Web Rule Language (SWRL) was used to implement rule-based rapid identification of risk factors, enabling stakeholders to query information in real-time and perform dynamic information updates promptly. To validate the effectiveness of ontology-based rule inferring for engineering quality risk management, a case study on a project in Guangzhou demonstrated that the proposed rule-inferring effectively identified risk factors and significantly reduced engineering quality risks. The ontology-based digital workflow optimized the engineering quality management workflow and contributed to more efficient and robust risk management practices. The findings provide a meaningful reference for advancing engineering quality risk management methods. Full article

This study investigates how born-digital memes about high-profile events can serve as rich archival resources for understanding contemporary cultural phenomena and public sentiment by using a linked-data framework. Using a mixed-method approach, this study analyzes memes from a high-profile trial through web scraping and linked-data structures to map themes, sentiments, and cultural references. The linked-data frame includes data collection and integration, semantic web technologies, ontology development, and API data access. The findings point to dominant narratives and shifting sentiment, which further illustrate how such memes reflect and contribute to the polarization of the societal discourse concerning the event. This research is relevant for understanding digital culture, exploring the archival potential of born-digital materials, and assessing the dynamics of public opinion in widely publicized cases. By showing the efficiency of linked data methodologies in the analysis of born-digital discourse, we add valuable insights to both digital humanities and social sciences, offering a new approach of studying ephemeral online content as cultural artifacts. Full article

Facing the severe global wildfire challenge and the need for advanced prediction, this study analysed the evolving research in forest fire prediction using an LDA-BERT similarity model. Due to climate change, human activities, and natural factors, forest fires threaten ecosystems, society, and the climate system. The vast existing literature on forest fire prediction makes it challenging to identify research themes manually. The proposed LDA-BERT model combines LDA and BERT. LDA was used for topic mining, determining the optimal number of topics by calculating the semantic consistency. BERT was employed in word vector training, using topic word probabilities as weights. The cosine similarity algorithm and normalisation were used to measure the topic similarity. Through empirical research on 13,552 publications from 1980–2023 retrieved from the Web of Science database, several key themes were identified, such as “wildfire risk management”, “vegetation and habitat changes”, and “climate change and forests”. Research trends show a shift from macro-level to micro-level studies, with modern technologies becoming a focus. Multidimensional scaling revealed a hierarchical theme distribution, with themes closely related to forest fires being dominant. This research offers valuable insights for the scientific community and policymakers, facilitating understanding these changes and contributing to wildfire mitigation. However, it has limitations like subjectivity in theme-representative word selection and needs further improvement in threshold setting and model performance evaluation. Future research can optimise these aspects and integrate emerging technologies to enhance forest fire prediction research. Full article

This research expands and updates a previous analysis of social entrepreneurship, incorporating the academic literature published between 2017 and 2024. Using the Web of Science database and Latent Semantic Analysis (LSA) technique, 1262 articles were analyzed, organized into three time periods (2017–2018, 2019–2021, and 2022–2024). The results show a clear evolution of the field, where sustainability, digitization, and resilience emerge as fundamental axes. Compared to the original research, which identified the convergence between the third sector and corporate social responsibility (CSR), this research reveals a consolidation of hybrid models, aligned with the Sustainable Development Goals (SDGs). Sustainability is positioned as a transverse axis, integrating economic, social, and environmental objectives. Digital transformation, driven by the pandemic, has facilitated scalability, organizational efficiency, and social impact measurement, but also poses challenges in terms of technological equity. In addition, organizational and community resilience takes center stage as an adaptive response to global crises. Research provides a comprehensive and up-to-date view of social entrepreneurship, identifying key trends and emerging challenges, while mapping new lines of research needed to strengthen the field in an increasingly globalized and technological world. Full article

Data science education is an interdisciplinary and multidisciplinary field, with curricula continually evolving to meet societal needs. This paper aims to report a bibliometric analysis focused on the pedagogical aspects and teaching/learning strategies employed in data science curriculum design, emphasizing contributions from key authors, publication sources, affiliations, content, and cited documents. The analysis draws on metadata from documents published over a 20-year period (2005–2024), encompassing a total of 1245 documents sourced from the Scopus scientific database. Additionally, a scoping review of 20 articles was conducted to identify key skills, topics, and courses in data science education. The findings reveal a growing interest in the field, with an increasingly multidisciplinary and interdisciplinary approach. Advances in artificial intelligence and related topics, such as linked data, the semantic web, ontologies, and machine learning, are shaping the development of data science curricula. The main challenges in data science education include the creation of up-to-date and competitive curricula, integrating data science training at early educational stages (K-12, secondary schools, pre-collegiate), leveraging data-driven technologies, and defining the profile of a data scientist. Furthermore, the availability of vast amounts of open, linked, and restricted data, along with advancements in data-driven technologies, is significantly influencing research in the field of data science education. Full article

This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios. Full article

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigour and is evaluated through quantitative metrics, validated using Google’s Rich Results Test API to confirm adherence to Google’s structured data guidelines. In this process, a “Pass” in the Rich Results Test is taken as an indication of eligibility for rich results, demonstrating the effectiveness of our generated structured data. The impact of our work is threefold: it advances the technological integration of a substantial segment of the web into the Semantic Web, promotes the adoption of Semantic Web technologies within the news sector, and significantly enhances the discoverability of news articles in aggregator platforms. Furthermore, our solution facilitates the broader dissemination of news content to diverse audiences. This submission introduces an innovative solution substantiated by empirical evidence of its impact and methodological soundness, thereby making a significant contribution to the field of Semantic Web research, particularly in the context of news and media articles. Full article

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

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article