Semantic Web Research Articles

Curriculum, Pedagogy, and Teaching/Learning Strategies in Data Science Education

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.

Enhancing Semantic Search Precision through the CBOW Algorithm in the Semantic Web

The Semantic Web enhances data interoperability and enables intelligent information retrieval through structured data representation. However, challenges remain in achieving high precision in semantic search. This paper uses the Continuous Bag of Words (CBOW) model to enhance semantic search precision. By generating rich word embeddings, CBOW enables a better understanding of contextual relationships among terms within semantic queries. Our approach has been evaluated using the websites intended to be used as a sample for testing the efficiency of semantic information retrieval, demonstrating significant improvements in search precision compared to traditional methods. The findings indicate that integrating CBOW into semantic search frameworks can lead to more relevant and accurate search results, paving the way for future advancements in Semantic Web technologies.

Semantic-Driven Paradigm Shift in Campus Guide Design Leveraging the KE-AIGC Framework

Campus guide systems are crucial to university infrastructure, shaping the experiences of students, staff, and visitors. Current systems face critical challenges in three areas: capturing diverse user needs, translating emotional requirements into design elements, and integrating campus cultural identity. This study integrates Kansei Engineering (KE) and Generative Artificial Intelligence (AIGC) to propose a semantic-driven design method. Using Semantic Web and Natural Language Processing (NLP), it models demand semantics, extracts emotional semantics such as safety and belonging, and maps them to design semantics for AIGC to generate personalized guide solutions. The approach leverages data-driven emotional semantic analysis and generative models to improve path guidance precision and cultural representation. Results indicate significant improvements in user experience, pathfinding accuracy, and cultural communication, with higher user satisfaction. This method provides a new semantic-driven pathway for developing campus guide systems and development prospects.

Knowledge Representation of Sensor Dataset with IoT Collaboration of Semantic Web and IoT: Storage of Temperature and Humidity Details

Introduction: Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data. Method: Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries. Result: This approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis. Conclusion: As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Evaluating the effectiveness of machine learning methods for keyword coverage using semantic data analysis

This article presents a comprehensive comparative analysis of two advanced hybrid machine learning approaches for keyword extraction: bidirectional encoder representations from transformers (BERT) combined with autoencoder (AE) and term frequency-inverse document frequency (TF-IDF) combined with autoencoder. The research targets the task of semantic analysis in text data to evaluate the effectiveness of these methods in ensuring adequate keyword coverage across diverse text corpora. The study delves into the architecture and operational principles of each method, with a particular focus on the integration with autoencoders to enhance the semantic integrity and relevance of the extracted keywords. The experimental section provides a detailed performance analysis of both methods on various text datasets, highlighting how the structure and semantic richness of the source data influence the outcomes. The evaluation methodology includes precision, recall, and F1-score metrics. The paper discusses the advantages and disadvantages of each approach and their suitability for specific keyword extraction tasks. The findings offer valuable insights for the scientific community, aiding in the selection of the most appropriate text processing method for applications requiring deep semantic understanding and high accuracy in information extraction.

Semantic composition of robotic solver algorithms on graph structures

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific computational building blocks that prevent code duplication and enable robots to adapt their software themselves. The envisaged algorithms are numerical solvers based on graph structures. In this article, we focus on kinematics and dynamics algorithms, but examples such as message passing on probabilistic networks and factor graphs or cascade control diagrams fall under the same pattern. The tools rely on mature standards from the Semantic Web. They first synthesize algorithms symbolically, from which they then generate efficient code. The use case is an overactuated mobile robot with two redundant arms.

Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospatial Data: A Framework Based on Ontology-Based Data Access

Abstract: 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.

TPepRet: a deep learning model for characterizing T cell receptors-antigen binding patterns.

T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions. To address this gap, we develop TPepRet, an innovative model that integrates subsequence mining with semantic integration capabilities. TPepRet combines the strengths of the Bidirectional Gated Recurrent Unit (BiGRU) network for capturing bidirectional sequence dependencies with the Large Language Model framework to analyze subsequences and global sequences comprehensively, which enables TPepRet to accurately decipher the semantic binding relationship between TCRs and peptides. We have evaluated TPepRet to a range of challenging scenarios, including performance benchmarking against other tools using diverse datasets, analysis of peptide binding preferences, characterization of T cells clonal expansion, identification of true binder in complex environments, assessment of key binding sites through alanine scanning, validation against expression rates from large-scale datasets, and ability to screen SARS-CoV-2 TCRs. The comprehensive results suggest that TPepRet outperforms existing tools. We believe TPepRet will become an effective tool for understanding TCR-peptide binding in clinical treatment. The source code can be obtained from https://github.com/CSUBioGroup/TPepRet.git. Supplementary data are available at Bioinformatics online.

Translating Natural Language questions into CIDOC-CRM SPARQL queries to access Cultural Heritage knowledge bases

To explore information on the Semantic Web, SPARQL queries or DL-queries are suitable tools. However, users interested in exploring the content of such knowledge bases often find it challenging to employ formal query languages, as this requires familiarity with the target domain’s representation model. To address these challenges, a Question-Answering System that automatically translates natural language questions into SPARQL queries, over the Smithsonian American Art Museum CIDOC-CRM representation is presented. The proposed approach uses an ontology, named Query Ontology, defined to represent the natural language concepts and relations specific to the question’s domain. This system’s architecture uses a traditional natural language processing symbolic approach, with a pipeline of modules for the syntactic, semantic, and pragmatic analysis. An evaluation of the proposed system is presented and shows very promising results.

An OWL-S based specification model of dynamic entity services for IoT

Purpose One of the open questions is how to define a complete semantic description of an Internet of Things (IoT) system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system. Design/methodology/approach One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. Findings One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system. Originality/value One of the open questions is how to define a complete semantic description of an IoT system and how to ensure the device’s identification in this type of system. To answer this question, the authors needed to propose a mechanism to describe an IoT system based on an IoT context ontology description. This mechanism is based on semantic web services creation to identify objects in IoT system. This paper aims to propose a model to describe the IoT system. The authors suggest an approach to describe each category of contextual information separately and ensure the adaptation in the IoT system.

Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration

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.

Ontologies in modelling and analysing of big genetic data

To systematize and effectively use the huge volume of experimental data accumulated in the field of bioinformatics and biomedicine, new approaches based on ontologies are needed, including automated methods for semantic integration of heterogeneous experimental data, methods for creating large knowledge bases and self-interpreting methods for analyzing large heterogeneous data based on deep learning. The article briefly presents the features of the subject area (bioinformatics, systems biology, biomedicine), formal definitions of the concept of ontology and knowledge graphs, as well as examples of using ontologies for semantic integration of heterogeneous data and creating large knowledge bases, as well as interpreting the results of deep learning on big data. As an example of a successful project, the Gene Ontology knowledge base is described, which not only includes terminological knowledge and gene ontology annotations (GOA), but also causal influence models (GO-CAM). This makes it useful not only for genomic biology, but also for systems biology, as well as for interpreting large-scale experimental data. An approach to building large ontologies using design patterns is discussed, using the ontology of biological attributes (OBA) as an example. Here, most of the classification is automatically computed based on previously created reference ontologies using automated inference, except for a small number of high-level concepts. One of the main problems of deep learning is the lack of interpretability, since neural networks often function as “black boxes” unable to explain their decisions. This paper describes approaches to creating methods for interpreting deep learning models and presents two examples of self-explanatory ontology-based deep learning models: (1) Deep GONet, which integrates Gene Ontology into a hierarchical neural network architecture, where each neuron represents a biological function. Experiments on cancer diagnostic datasets show that Deep GONet is easily interpretable and has high performance in distinguishing cancerous and non-cancerous samples. (2) ONN4MST, which uses biome ontologies to trace microbial sources of samples whose niches were previously poorly studied or unknown, detecting microbial contaminants. ONN4MST can distinguish samples from ontologically similar biomes, thus offering a quantitative way to characterize the evolution of the human gut microbial community. Both examples demonstrate high performance and interpretability, making them valuable tools for analyzing and interpreting big data in biology.

About Applying Semantic Web Technology for Information Modeling in Construction

The article provides justification for using Semantic Web technology to describe an information model of a capital construction project. The proposed technology is compared with current approaches of information modeling.

A Data-Driven Approach to Estimating the Impact of Audiovisual Art Events Through Web Presence

In the modern era, a major avenue of dissemination for cultural and artistic events is through the World-Wide Web, where every such event has a multifaceted distinct digital footprint. This digital footprint is an indicator of how strong the influence of each event is in the public’s perception and to what extent it becomes part of the audiovisual art landscape. This study aims to present how the impact of an audiovisual event may be estimated using quantitative data collected through its online presence. This data-driven approach is made possible through web data extraction techniques and the use of generative artificial intelligence, which allows for structured information extraction from an endless variety of websites. Based on an event’s innate characteristics, web outreach, estimated scope, and thematical popularity, an encompassing impact factor is calculated, which may be used to rank events on the basis of perceived influence. For the purposes of this study, a dataset consisting of thousands of events in Greece was collected over an extended period. These data were used for a computational statistical analysis. Through this process of data collection, impact calculation, and analysis, data-driven insights were derived concerning the landscape of audiovisual art events.

Analysis of Studies on Interpreter’s Role on International Journals by Citespace (2000-2022)

Interpreters play important roles in international communication, and the international research on interpreter’s role has a history of over 50 years. This study use Citespace tool to analyse the data of Web of Science from 2000 to 2022 in order to understand the status quo and trend of research. It is found that such research saw a general trend of increase in fluctuation and there seems to be lack of cooperative research with much room for future cooperation between different research institutions. Current research can be categorized into conference interpreting, medical interpreting, court interpreting, prison interpreting, community interpreting, sign language interpreting, telephone interpreting, etc. It can also be classified into professional and non-professional interpreting, self-perspective and others ’ perspective, interpreting practice, interpreting quality, interpreting history, professional and inter professional education, etc. Future research could further the traditional domains and integrate the research with other domains of interpreting studies and that of other sectors. It could be further combined with sociological theories, adopt more diversified research methods, take a broader view in terms of interpreter’s working environment, and combine more closely with technology.

Efficient guided inpainting of larger hole missing images based on hierarchical decoding network

When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. In this paper, a novel hierarchical decoding network for image inpainting is proposed. Firstly, the structural priors extracted from the encoding layer are utilized to guide the first decoding layer, while residual blocks are employed to extract deep-level image features. Secondly, multiple hierarchical decoding layers progressively fill in the missing regions from top to bottom, then interlayer features and gradient priors are used to guide information transfer between layers. Furthermore, a proposed Multi-dimensional Efficient Attention is introduced for feature fusion, enabling more effective extraction of image features across different dimensions compared to conventional methods. Finally, Efficient Context Fusion combines the reconstructed feature maps from different decoding layers into the image space, preserving the semantic integrity of the output image. Experiments have been conducted to validate the effectiveness of the proposed method, demonstrating superior performance in both subjective and objective evaluations. When inpainting images with missing regions ranging from 50% to 60%, the proposed method achieves improvements of 0.02 dB (0.22 dB) and 0.001 (0.003) in PSNR and SSIM, on the CelebA-HQ (Places2) dataset, respectively.

Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models

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.

Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management

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.

A Review on Marathi Braille

This paper investigates the complex process of translating Marathi text into Bharati Braille, emphasizing the critical importance of equitable information access for individuals with visual impairments. It provides an in-depth analysis of Bharati Braille's structure, encoding guidelines, and its adaptation to Indian languages, with a particular focus on Marathi's linguistic features and challenges. Through a comprehensive literature review, the study examines existing text-to-Braille conversion technologies, analyzing hardware and software solutions and their applicability to Marathi. Key challenges such as linguistic nuances, dialectical variations, and the preservation of semantic integrity during translation are explored in detail. The paper highlights technical barriers, including encoding complexities and readability issues, and stresses the need for innovative solutions to address these gaps. By offering actionable insights, this research supports the development of advanced text-to-Braille systems tailored to Marathi, fostering inclusivity and enhancing access to education and information for visually impaired individuals. This study aims to contribute to the broader goal of promoting social and educational inclusion, empowering individuals with visual impairments to participate fully in society.

Generative AI in Artistic Enterprises

Generative AI has revolutionized the field of digital art by enabling automated image creation that mimics human artistry. Traditional approaches often need more thematic coherence and help to capture complex styles in artistic domains. In order to overcome these constraints, we present a Generative Adversarial Network (GAN) model integrated with a Semantic Web architecture, therefore providing automated painting creation for creative businesses. Our strategy guarantees thematic and stylistic congruence by using Semantic Web methods to include contextual information and GANs to create high-quality paintings from noise. Our GAN produces reasonable and varied creative outputs. Experimental findings show how flexible the model is for different artistic topics, therefore helping applications in the creative sectors via improved AI-driven artistic production.