Semantic Web Research Articles

A survey on semantic data management as intersection of ontology-based data access, semantic modeling and data lakes

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Such approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies.

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Semantic Data Integration and Querying: A Survey and Challenges

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.

BOTSH: Towards aligning semantic web of things with building ontologies

The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts.

Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces

The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration.

Processing reduced speech in the L1 and L2: a combined eye-tracking and ERP study

ABSTRACT We examined the cognitive processes underlying the comprehension of reduced word pronunciation variants in natives and advanced learners of French. In a passive listening visual world task, participants heard sentences containing either a reduced or a full form and saw pictures representing the target word, a phonological competitor and two neutral distractors. After each sentence they saw a picture and had to decide whether it matched the content of that sentence. Eye movements and EEG were recorded simultaneously. Because the two recordings offer complementary information about cognitive processes, we developed methods for analysing the signals in combination. We found a stronger effect of reduction on phonetic processing and semantic integration in learners than in natives, but the effects are different from the N100/N400 and P600 effects found in previous research. Time-locking EEG signals on fixation moments in the eye movements offers a window onto the time course of semantic integration.

WEB MINING TO DETECT ONLINE SPREAD OF TERRORISM

To fight online terrorism, integrating web mining techniques with data mining algorithms is crucial. Various algorithms like Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forests (RF), and Gradient Boosting (GB) are studied for detecting terrorist activities online, extracting data, identifying patterns, and relevant information. Logistic Regression provides a probabilistic framework, KNN uses similarity metrics, SVM constructs hyper-planes, NB assumes feature independence, DT builds decision trees, RF applies ensemble learning, and GB boosts weak learners' performance. These algorithms aid in proactive monitoring and prevention of online terrorism through efficient analysis of structured and unstructured web data. By combining web mining and data mining strengths, this study emphasizes a comprehensive approach to combatting online terrorism dissemination, helping security agencies anticipate evolving threats and prevent terrorist propagation effectively. Key Words: Terrorism, Naive-bayes, random forest, web mining, Gradient boosting

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.

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

This paper studies the geological disaster emergency command and management system based on geographic information system. This system is for the province, city, county land resources and other aspects of the comprehensive application, the realization of real-time information display, meteorological early warning, group measurement and mass prevention, monitoring and early warning, major disaster management, prevention plan, quick report processing, emergency investigation, emergency disposal, emergency consultation and other functions. Through the GSM network, the monitoring data is sent to the monitoring center through the mobile phone, and then the monitoring center processes, stores, displays and alarms the monitoring data. Finally, B/S structure is used to build a remote Web data service publishing platform, so that it can carry out real-time monitoring at any place, and improve the speed, efficiency and intuition of high-speed slope disaster. It is proved that the method has the advantages of stable operation, accurate data, fast transmission speed and high monitoring accuracy through the practical application of a highway project foundation pit slope monitoring.

Analyzing Code: What a Critical Code Studies Approach Reveals About the Epistemology of Data Journalism

The last decade has seen consolidation of data journalism—defined broadly as reporting involving the collection, analysis, and presentation of quantitative datasets—as an established subfield of journalism. While programming code underlies much of data journalism, few studies focus on it, and almost none examine the code itself. Informed by Critical Code Studies, this study departs from previous efforts by examining the role of both quantitative data and computational analysis in data-driven reporting. Using a sample of 234 GitHub repositories of published news stories in the US, we conduct a content analysis of three separate elements of each story: code, data, and the published article. We then use hierarchical clustering to triangulate across these characteristics and identify groups of stories for qualitative review. Our analysis demonstrates the breadth of data journalism—from major investigations to quick statistical reports—enabled by the combination of abundant data and computational techniques to collect and process that data. We highlight the continued dependence on government data, but also the labor involved in scraping web data, and the use of sophisticated reverse-engineering methods to scrutinize tech corporations. Finally, we make several observations regarding studying GitHub as a data journalism infrastructure.

SEIR‐driven semantic integration framework: Internet of Things‐enhanced epidemiological surveillance in COVID‐19 outbreaks using recurrent neural networks

AbstractWith the current COVID‐19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state‐of‐the‐art SEIR‐Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID‐19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real‐time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID‐19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R‐squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R‐squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.

A Study on Understanding the Mediation of Web 3.0 Technologies in The Teaching-Learning Process and its effect on the Student's Achievement

An abstract summarizes, in one paragraph (usually), the major aspects of the entire paper in the following This paper provides a thorough examination of the function of Web 3.0 technologies in facilitating the teaching-learning process and its influence on student academic performance. Web 3.0, distinguished by its focus on machine learning and artificial intelligence, has revolutionized the conventional education environment, providing novel opportunities for interactive and customized learning encounters. This research conducts a comprehensive analysis of current literature to explore the several methods by which Web 3.0 technologies, including semantic web, social networking, and virtual reality, are being incorporated into educational environments. Moreover, the paper investigates the impact of these technologies on student involvement, motivation, and academic achievement. The findings indicate that using Web 3.0 technologies in education has the capacity to improve student learning outcomes through the provision of individualized and interactive learning experiences. Nevertheless, it is imperative to tackle obstacles such as the digital divide and privacy issues in order to guarantee fair and equal access and utilization. This study provides recommendations for future research and practical consequences for educators and policymakers who want to utilize Web 3.0 technologies to improve teaching and learning processes

Artificial Intelligence in Academic Translation: A Comparative Study of Large Language Models and Google Translate

Purpose. The advent of Large Language Model (LLM), a generative artificial intelligence (AI) model, in November 2022 has had a profound impact on various domains, including the field of translation studies. This motivated this study to conduct a rigorous evaluation of the effectiveness and precision of machine translation, represented by Google Translate (GT), in comparison to Large Language Models (LLMs), specifically ChatGPT 3.5 and 4, when translating academic abstracts bidirectionally between English and Arabic. 
 Methods. Employing a mixed-design approach, this study utilizes a corpus comprising 20 abstracts sourced from peer-reviewed journals indexed in the Clarivate Web of Science, specifically the Journal of Arabic Literature and Al-Istihlal Journal. The abstracts are equally divided to represent both English-Arabic and Arabic-English translation directionality. The study’s design is rooted in a comprehensive evaluation rubric adapted from Hurtado Albir and Taylor (2015), focusing on semantic integrity, syntactic coherence, and technical adequacy. Three independent raters carried out assessments of the translation outputs generated by both GT and LLM models. 
 Results. Results from quantitative and qualitative analyses indicated that LLM tools significantly outperformed MT outputs in both Arabic and English translation directions. Additionally, ChatGPT 4 demonstrated a significant advantage over ChatGPT 3.5 in Arabic-English translation, while no statistically significant difference was observed in the English-Arabic translation directionality. Qualitative analysis findings indicated that AI tools exhibited the capacity to comprehend contextual nuances, recognize city names, and adapt to the target language's style. Conversely, GT displayed limitations in handling specific contextual aspects and often provided literal translations for certain terms.

Towards a Digital Representation of Building Systems Controls

Energy monitoring and performance optimization processes play a significant role in ensuring energy-efficient building operation. However, the current implementation of these processes requires substantial time and effort due to the fragmented and non-digital nature of technical building equipment information. To address these challenges, we demonstrate the application of the PLCont methodology within an ongoing monitoring process in a real building. PLCont utilizes Semantic Web Technologies and established RDF ontologies to describe the control topology. The digital representation of control functions aligns closely with the IEC standard 61131-3, and the PLCont ontology establishes a connection between the topology and the control functions. We compare the conventional approach and the PLCont methodol-ogy using the supply air temperature control of an Air Handling Unit (AHU) as a use case. A graphical user interface built upon the PLCont backend provides a comprehensive represen-tation of the system's functionality. It displays the system topology as a digital HVAC schema, the function code exported from the PLC, time-series data plots, and textual descriptions of implemented functions. We demonstrate how this approach enhances transparency and facil-itates the identification and elimination of faulty system behavior, ultimately contributing to the optimization and energy efficiency of building systems.

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

Strategic Analysis in the Public Sector using Semantic Web Technologies

This article addresses the complex challenges that public organizations face in designing, implementing, and evaluating their strategies, where public interest and regulatory compliance often intertwine with strategic objectives. This research investigates the application of ontologies in the field of public sector strategy management to enhance the capacity of organizations to make informed data-driven decisions, efficiently allocate resources, and effectively navigate the intricate landscape of the public sector. The LNEC - National Laboratory for Civil Engineering’s strategy is used as an exploratory case study. Semantic web technologies are used to perform strategy analysis, including validating the strategy formulation, and supporting the strategy execution by assessing performance indicators, verifying the design of cause-and-effect relationships between strategic objectives, and monitoring and empirically validating these relationships. The increased interoperability of these technologies enables information sharing across systems and organizations. Following the strategy analysis, recommendations are provided, leading to a more robust and data-driven strategic management approach, enabling accurate, traceable, and continuous monitoring of an organization’s strategy. Theoretical and practical implications are discussed, along with limitations and future work. This research offers a blueprint for public sector organizations seeking to optimize their strategies, foster transparency, and deliver more effective services to the public they serve.

Effect of spectral degradation on speech intelligibility and cortical representation.

Noise-vocoded speech has long been used to investigate how acoustic cues affect speech understanding. Studies indicate that reducing the number of spectral channel bands diminishes speech intelligibility. Despite previous studies examining the channel band effect using earlier event-related potential (ERP) components, such as P1, N1, and P2, a clear consensus or understanding remains elusive. Given our hypothesis that spectral degradation affects higher-order processing of speech understanding beyond mere perception, we aimed to objectively measure differences in higher-order abilities to discriminate or interpret meaning. Using an oddball paradigm with speech stimuli, we examined how neural signals correlate with the evaluation of speech stimuli based on the number of channel bands measuring N2 and P3b components. In 20 young participants with normal hearing, we measured speech intelligibility and N2 and P3b responses using a one-syllable task paradigm with animal and non-animal stimuli across four vocoder conditions with 4, 8, 16, or 32 channel bands. Behavioral data from word repetition clearly affected the number of channel bands, and all pairs were significantly different (p < 0.001). We also observed significant effects of the number of channels on the peak amplitude [F(2.006, 38.117) = 9.077, p < 0.001] and peak latency [F(3, 57) = 26.642, p < 0.001] of the N2 component. Similarly, the P3b component showed significant main effects of the number of channel bands on the peak amplitude [F(2.231, 42.391) = 13.045, p < 0.001] and peak latency [F(3, 57) = 2.968, p = 0.039]. In summary, our findings provide compelling evidence that spectral channel bands profoundly influence cortical speech processing, as reflected in the N2 and P3b components, a higher-order cognitive process. We conclude that spectrally degraded one-syllable speech primarily affects cortical responses during semantic integration.

A multi-attention residual integrated network with enhanced fireworks algorithm for remote sensing image classification

This research examines a multi-attention residual integrated network with an enhanced fireworks algorithm for remote sensing image classification. Remote sensing (RS) picture classification is important for land cover mapping, environmental monitoring, and urban planning. Remote sensing image classification is important in earth observation since the military and commercial sectors have focused on it. Due to RS data's high complexity and limited labelled examples, classifying RS pictures is difficult. Deep Learning (DL) techniques have made great strides in RS image categorization, expanding this field's potential. This research introduces Multi-Attention Residual Integrated Network with Enhanced Fireworks Algorithm (MAR-EFA) to improve hyper spectral image identification. MARIN-EFA improves feature fusion and removes unneeded features to overcome technique constraints. The suggested method weights features using different attention models. These characteristics are then carefully extracted and integrated using a residual network. Final contextual semantic integration on deeply fused features is done with a Bi-LSTM network. Our population-based Enhanced Fireworks Algorithm (EFA) is inspired by fireworks' explosive performance and optimises MARIN parameters. Attention techniques and an improved optimisation algorithm improve performance over current systems. Numerous Eurosat dataset studies were assessed using various performance indicators. The simulation results show that MARIN-EFA outperforms current methods. The suggested technique shows promise for improving RS picture classification and allowing more accurate and reliable data categorization.

Analyzing and building information resources to support decision making in educational systems

The author discusses the analysis and building of information resources for decision-making systems and efficient business processes management within the framework of the operating digital twin of educational institution. She reviews the general approaches to building the knowledge bases of the decision-making systems, and identifies the factors to be taken into account when building the databases. The author discusses literature analysis and monitoring tools, analyzes the publication activity in science periodicals and provides the data of sematic charts monitoring separately for 2015 and 2020 in several areas: education computerization, continuing education, education digitalization, remote learning systems, digital courses, digital education, electronic libraries, information and communication technologies, massive open online courses and virtual education. The data of Web of Science were used. The semantic charts are built on the basis of co-occurence of scholarly sources keywords using VOSviewer software that extracts terms from abstracts and article keywords and adding them to the entity

Optimizing Data Extraction using Preprocessing for Enhanced Efficiency

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web.&#x0D; To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications.&#x0D; In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs.&#x0D; Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.