Semantic Web Research Articles

Retraction Note: A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in semantic web

Retraction Note: A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in semantic web

Hierarchical clustering algorithm to generate ontology network

Ontologies have played a crucial role in addressing challenges related to knowledge modeling, artificial intelligence, clustering, and classification. Within the realm of web services, ontologies primarily serve to describe web service interfaces, facilitating the automation of functional processes, particularly to guide the registers in discovering services. Enriching this model with a shared ontology (knowledge base) can improve the efficiency of web services technology by guiding web services processes. This shared ontology plays the role of intermediary between queries and registries, and can be shared between different registries. However, manually constructing ontologies is a labor-intensive and expensive endeavor, necessitating domain expertise. Automatic ontology construction approaches present compelling alternatives, offering advantages in terms of both time and financial costs. In this article, we advocate for the utilization of the Hierarchical Clustering algorithm to generate an ontological network (Service Ontology (SO)) from a semantic web services corpus. Experimental validations are presented to demonstrate the effectiveness of the proposed methodology.

ARIMAX-Threshold 모형을 이용한 20대 이하 마약류 사범 검거 수 시계열 분석

In recent years, the number of drug offenders under 20s has surged, accounting for approximately 35.5% of all drug offenders in 2023, which becomes a serious social issue in Korea. The purpose of this work is to study and compare time series models for statistical analysis and prediction for the number of drug offenders under 20s. ARIMA and ARIMAX model is applied to the data of the number of drug offenders arrested under 20s. In order to further improve the predictability, a model added by threshold, reflecting the presence or absence of the exogenous variable, is proposed and compared. As for the exogenous variable, dark web data that is directly related to the number of drug offenders arrested, as a main drug dealer, is used. Based on cross-correlation coefficient analysis, the ARIMAX-Threshold model including indicator variables and threshold is proposed. Out-of-sample forecasting analysis is performed to compare the forecasting performance, and it is revealed that the ARIMAX-Threshold model proposed in this study improves the forecasting errors by up to 9.4%. The predicted values and prediction intervals of the data on the number of drug offenders under 20s until April 2025 are presented, which will contribute to help in policy development for drug crime prevention.

N400 or P600?—A Systematic Review of ERP Studies on Gender Stereotype Violations

ABSTRACTPsycholinguistic studies using the event‐related brain potential (ERP) technique have found both N400 and P600 effects for gender stereotype violations. The finding of a P600 effect for this type of world knowledge violation is surprizing given that this component is traditionally associated with syntactic violations. In this paper, we set out to systematically analyse design‐ and task patterns of ERP studies investigating gender stereotype violations. Based on our review, we propose a scheme that predicts a P600 effect for gender stereotype violations for stimuli comprising entire sentences, and specifically when the gender stereotype serves to establish coherence in inferences (i.e., expecting a specific referent gender or a stereotype‐appropriate behaviour of the sentence subject based on the gender stereotype information). We predict an N400 effect for gender stereotype information that does not serve to establish coherence in inferences, as well as in priming paradigms. By extension, our predictive scheme suggests that the N400 may reflect a hybrid process of semantic retrieval and integration, while the P600 may reflect a cognitive process of error monitoring, and mental revision. Our study can aid in the interpretation of previous findings and inform future studies investigating gender stereotyping.

Efficient capture of dry eye data from the real world: The Save Sight Dry Eye Registry

PurposeTo describe a web-based, high-quality data collection tool able to track the clinical data of patients with dry eye disease (DED) in routine clinical practice. DesignRetrospective analysis of core system web data from a prospectively designed, observational, routine clinical practice registry, the Save Sight Dry Eye Registry (SSDER). MethodsPatients with DED, from 11-Nov-2020 to 04-Mar-2024 were analysed. Ocular Surface Disease Index (OSDI) and Ocular Comfort Index (OCI) questionnaires collected patient-reported DED symptoms, and the Patient Health Questionnaire-4 (PHQ-4) screened for anxiety and depression. Outcome data include index visit demography, DED symptoms and signs. ResultsFifteen clinicians (ophthalmologists and optometrists) from nine practices across Australia, France, Germany, Nepal, Spain and the United Kingdom contributed data, comprising index visits of 958 eyes from 479 patients (mean±SD age 56±17 years; 78.7% female). Up to 89.9% of the patients had either evaporative or mixed DED based on clinician's judgement. Mean OSDI symptom score at index visit was 35.7 ± 19.4 (n = 366), and the mean sores for frequency and intensity of discomfort with the OCI was 31.9 ± 6.1 and 31.4 ± 6.8 (n = 202) respectively. Forty-one percent of patients had mild to severe symptoms of anxiety and depression. Median tear breakup time and tear meniscus height were 5 (IQR 2–8) seconds and 0.3 (IQR 0.2–0.4) mm, respectively. Ocular surface staining was graded as none (37.6%), minimal (31.7%), mild (19.8%), moderate (8.8%) and severe (2.1%). ConclusionThe SSDER facilitated the collection of data from patients with DED from real-world clinical practice. Presenting patients had moderate DED, which was mostly evaporative in nature. Symptoms of anxiety and depression were reported by more than one-third of the cohort.

Ontology-based inference decision support system for emergency response in tunnel vehicle accidents

Emergency response plans for tunnel vehicle accidents are crucial to ensure human safety, protect critical infrastructure, and maintain the smooth operation of transportation networks. However, many decision-support systems for emergency responses still rely significantly on predefined response strategies, which may not be sufficiently flexible to manage unexpected or complex incidents. Moreover, existing systems may lack the ability to effectively respond effectively to the impact different emergency scenarios and responses. In this study, semantic web technologies were used to construct a digital decision-support system for emergency responses to tunnel vehicle accidents. A basic digital framework was developed by analysing the knowledge system of the tunnel emergency response, examining its critical elements and intrinsic relationships, and mapping it to the ontology. In addition, the strategies of previous pre-plans are summarised and transformed into semantic rules. Finally, different accident scenarios were modelled to validate the effectiveness of the developed emergency response system.

Improving Robustness in IoT Malware Detection through Execution Order Analysis

The rapid expansion of the Internet of Things (IoT) has significantly increased the prevalence of malware targeting IoT devices. Although machine learning models offer promising solutions for automatic malware detection, they are increasingly vulnerable to adversarial attacks. These attacks exploit the model’s feedback loop to iteratively refine malware, producing adversarial samples that evade detection. As such, enhancing the robustness of these models is of paramount importance. Our research introduces a novel approach to bolster malware detection by retaining additional semantic information within the execution order analysis of malware programs. The method significantly improves the resilience of detection models against adversarial samples and implements two adversarial attack methods to rigorously test our model’s robustness by generating authentic adversarial examples for validation. We highlight the critical impact of preserving semantic integrity in malware detection and present a solution to counteract the growing threat of adversarial attacks in IoT environments.

Building a web-based Comprehensive School Portal System Using the CodeIgniter Framework

With the various advantages and conveniences that the internet can provide, various fields of human activity have begun to use internet technology to help facilitate the activities they undertake. Likewise with the education sector in managing its administration. The web is one of the internet technologies that is widely used to facilitate monitoring of school activities because it can be accessed easily anywhere and at any time, from school facilities to school activities. Through the use of a web data system, problems involving redundancy of student information, insynchronization between existing information and slow production of information can be overcome. By designing an information system or web-based portal, you can increase performance effectiveness.

Enhancing Martial Arts Safety Through 6G-Enabled Risk Assessment and Alert Systems

This paper proposes an advanced approach to enhance athlete safety in martial arts competitions by integrating 6G-enabled technologies with enterprise engineering and semantic integrity principles. Wearable sensors within a 6G network capture vital physiological data, such as temperature, pulse rate, opponent-induced force, and ECG signals. These data are analyzed using an Optimized Support Vector Risk Prediction Model (OSVRPM), optimized through the Vinegar Fly Optimization Algorithm (VFOA), to accurately assess athlete risk levels during competitions. Real-time alert messages, facilitated by the 6G wireless network, are disseminated to medical personnel, judges, referees, and athletes upon identifying heightened risk, ensuring swift intervention and reinforcing safety protocols in martial arts tournaments. This innovative integration exemplifies the application of modeling with enterprise engineering and semantic integrity to safeguard athletes in dynamic competitive environments.

The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys

The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys

Social and Web Data Framework for Understanding Loneliness.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework's utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm.

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

From Clinical Information Systems to Personalized Health Knowledge Graphs.

This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.

Data and Process Harmonisation of Multi-National, Multi-Site Research Data.

To achieve a single fully harmonised research data set suitable for analysis from data collected at multiple sites requires not only semantic integration of collection concepts and convergence onto single collection units, but harmonisation of data collection processes. We describe our experience of identifying harmonisation challenges in the Precision ALS project, with particular focus on process alignment challenges in a multi-site multi-national research data collection project.

Integration of Google Trends Data in Predicting New HIV Cases in Western Visayas, the Philippines

Recent studies have demonstrated the capacity of Internet search data to be used for monitoring and tracking infectious diseases. Given that the Philippines has one of the most rapidly expanding human immunodeficiency virus (HIV) epidemics in the globe, it is imperative to enhance the local surveillance and prediction of HIV by utilizing easily accessible web data such as Google Trends. This study aims to [1] evaluate the advantages of utilizing Google Trends data (GTD) in predicting new monthly HIV diagnoses in the Western Visayas and [2] determine the suitable modeling approach that effectively utilizes this tool in forecasting HIV cases in the region. Data on new monthly HIV cases from January 2012–December 2022 in Western Visayas was requested from the Department of Health Region VI Office and HIV-related internet search data was obtained from Google Trends. Data imputations were applied to mitigate the effects of COVID-19 on HIV data. The results of the traditional seasonal autoregressive integrated moving average (SARIMA) model were compared with the results of the neural network autoregression (NNAR) model using cross-validation via rolling forecast origin. GTD have a moderately weak but significant association with HIV case numbers in Western Visayas. NNAR models outperformed the traditional SARIMA models in terms of mean absolute percentage error, mean absolute error, and root mean squared error. The use of Google search data in NNAR models improved the predictive accuracy of the forecasts when using moving average imputation as a replacement for outliers. However, this does not accurately represent the overall NNAR process, as the linear interpolation replacements did not yield comparable outcomes. This study showed that, if suitable models are adopted, utilizing HIV-related web data has the potential to produce real-time surveillance systems for HIV monitoring and forecasting. This presents an opportunity to enhance local surveillance in the country not just for HIV but also for other infectious diseases such as tuberculosis and dengue. Internet search data is free and readily available, and public health agencies can adopt this type of modeling, even with limited funding; however, further study is advised.

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.

Semantic Web Insights Into the Classification of Folk Paper-Cut Cultural Genes

This study aims to classify folk paper-cut patterns by regional culture, leveraging Semantic Web and LSTM technologies to discern how these patterns reflect distinct cultural characteristics. By developing an LSTM model capable of recognizing and categorizing these patterns, our study not only demonstrates high accuracy in classifying regional cultural genes but also reveals the depth of cultural heritage embedded in paper-cut art. The findings underscore the potential of computational methods in understanding and preserving the rich tapestry of cultural expressions through paper cuts. This work sets a foundation for future explorations into the digital preservation of cultural heritage, highlighting the critical role of technology in safeguarding and interpreting traditional arts in the context of regional culture.

OntoXAI: a semantic web rule language approach for explainable artificial intelligence

OntoXAI: a semantic web rule language approach for explainable artificial intelligence

Categorical analysis of logic in conceptual modeling of subject areas for semantic integrity of information resources

Categorical analysis of logic in conceptual modeling of subject areas for semantic integrity of information resources

Knowledge Graphs and Semantic Web Tools in Cyber Threat Intelligence: A Systematic Literature Review

Knowledge Graphs and Semantic Web Tools in Cyber Threat Intelligence: A Systematic Literature Review