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Application of Semantic Technologies in Intelligent Environment
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Application of Semantic Technologies in Intelligent Environment

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 11538

Special Issue Editors

Prof. Dr. Maria Nisheva-Pavlova

E-Mail Website
Guest Editor
Faculty of Mathematics and Informtics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
Interests: knowledge representation and engineering; semantic technologies; machine learning; digital libraries
Prof. Dr. Galia Angelova

E-Mail Website
Guest Editor
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 25A Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
Interests: NLP; big data and data analytics; applications of artificial intelligence for smart society
Dr. Moulay A. Akhloufi

E-Mail Website
Guest Editor
Perception, Robotics, and Intelligent Machines Research Group (PRIME), Dept of Computer Science, Université de Moncton, Moncton, NB, E1A 3E9, Canada
Interests: machine learning; deep learning; computer vision; robotics; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a new Special Issue of Big Data and Cognitive Computing titled “Application of Semantic Technologies in Intelligent Environment”.

Semantic technology is a set of knowledge modeling methods, standards and tools that help intelligent agents to understand language and process information in a way similar to how humans do. Semantic technologies can automatically store, manage and retrieve information based on its meaning and logical relationships. They provide advanced means for categorizing and processing data, as well as for discovering relationships within various data sets.

The primary goal of semantic technologies is to help computers understand data and thereby to facilitate the reuse of information and provide semantic interoperability between heterogeneous information systems.

Semantic technologies are “meaning-centered”. They involve a lot of generic applications, such as the encoding and decoding of semantic representation; the automatic recognition of topics and concepts; semantic annotation and the semantic enhancement of data sets; semantic data integration; information and meaning extraction; semantic search, etc. To enable the encoding of meaning with the data, appropriate technologies include RDF/RDFS and OWL, which rely on embedded semantics and provide convenient tools for describing ontologies. Embedded semantics of data offers significant advantages, such as reasoning on data and extracting concepts and associations between concepts in text.

The widespread penetration of large language models has made the task of their integration with modern semantic technologies particularly relevant in order to obtain a maximum synergistic effect. We hope that this Special Issue will contribute in that direction.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Ontology engineering and ontology mediation;
  • Linked data and their applications;
  • Automatic metadata extraction;
  • Automatic generation of knowledge graphs from texts;
  • Semantic search;
  • Semantic interoperability of information systems;
  • Semantic enhancement of big data;
  • Big data semantics, search and mining;
  • Integrating large language models and knowledge graphs;
  • Semantic technologies for intelligent urban environment;
  • Semantic technologies in eHealth;
  • Semantic technologies for intelligent agriculture;
  • Semantic technologies for finance and administration;
  • Semantic technologies in education;
  • Semantic technologies in the energy sector.

We look forward to receiving your contributions.

Prof. Dr. Maria Nisheva-Pavlova
Prof. Dr. Galia Angelova
Dr. Moulay A. Akhloufi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • semantic web
  • linked data
  • knowledge graph
  • ontology
  • big data
  • information retrieval
  • semantic interoperability

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Published Papers (4 papers)

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Research

28 pages, 1129 KiB  
Article
Mass Generation of Programming Learning Problems from Public Code Repositories
by Oleg Sychev and Dmitry Shashkov
Big Data Cogn. Comput. 2025, 9(3), 57; https://doi.org/10.3390/bdcc9030057 - 28 Feb 2025
Viewed by 518
Abstract
We present an automatic approach for generating learning problems for teaching introductory programming in different programming languages. The current implementation allows input and output in the three most popular programming languages for teaching introductory programming courses: C++, Java, and Python. The generator stores [...] Read more.
We present an automatic approach for generating learning problems for teaching introductory programming in different programming languages. The current implementation allows input and output in the three most popular programming languages for teaching introductory programming courses: C++, Java, and Python. The generator stores learning problems using the “meaning tree”, a language-independent representation of a syntax tree. During this study, we generated a bank of 1,428,899 learning problems focused on the order of expression evaluation. They were generated in about 16 h. The learning problems were classified for further use with the used concepts, possible domain-rule violations, and required skills; they covered a wide range of difficulties and topics. The problems were validated by automatically solving them in an intelligent tutoring system that recorded the actual skills used and violations made. The generated problems were favorably assessed by 10 experts: teachers and teaching assistants in introductory programming courses. They noted that the problems are ready for use without further manual improvement and that the classification system is flexible enough to receive problems with desirable properties. The proposed approach combines the advantages of different state-of-the-art methods. It combines the diversity of learning problems generated by restricted randomization and large language models with full correctness and a natural look of template-based problems, which makes it a good fit for large-scale learning problem generation. Full article
(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)
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19 pages, 14471 KiB  
Article
Efficient Data Augmentation Methods for Crop Disease Recognition in Sustainable Environmental Systems
by Saebom Lee and Sokjoon Lee
Big Data Cogn. Comput. 2025, 9(1), 8; https://doi.org/10.3390/bdcc9010008 - 8 Jan 2025
Viewed by 7965
Abstract
Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse [...] Read more.
Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse crops and disease patterns. To address these challenges, we propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition. By constructing a new large-scale dataset comprising 24 different classes, including both fruit and leaf samples, we intend to handle a variety of disease patterns and improve model generalization capabilities. Geometric transformations and color space augmentation techniques are applied to validate the efficiency of deep learning models, specifically convolution and transformer models, in recognizing multiple crop diseases. The experimental results show that these augmentation techniques improve classification accuracy, achieving F1 scores exceeding 98%. Feature map analysis further confirms that the models effectively capture key disease characteristics. This study underscores the importance of data augmentation in developing automated, energy-efficient, and environmentally sustainable crop disease detection solutions, contributing to more sustainable agricultural practices. Full article
(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)
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27 pages, 3289 KiB  
Article
Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics
by Carlo Galli, Maria Teresa Colangelo, Marco Meleti, Stefano Guizzardi and Elena Calciolari
Big Data Cogn. Comput. 2025, 9(1), 7; https://doi.org/10.3390/bdcc9010007 - 5 Jan 2025
Viewed by 790
Abstract
Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands [...] Read more.
Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. In this study, we employed BERTopic, a transformer-based topic modeling framework, to map the thematic landscape of periodontics research published in MEDLINE from 2009 to 2024. We identified 31 broad topics encompassing four major thematic axes—patient management, periomedicine, oral microbiology, and implant-related surgery—thereby illuminating core areas and their semantic relationships. Compared with a conventional Latent Dirichlet Allocation (LDA) approach, BERTopic yielded more contextually nuanced clusters and facilitated the isolation of distinct, smaller research niches. Although some documents remained unlabeled, potentially reflecting either semantic ambiguity or niche topics below the clustering threshold, our results underscore the flexibility, interpretability, and scalability of neural topic modeling in this domain. Future refinements—such as domain-specific embedding models and optimized granularity levels—could further enhance the precision and utility of this method, ultimately guiding researchers, educators, and policymakers in navigating the evolving landscape of periodontics. Full article
(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)
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28 pages, 5843 KiB  
Article
An Analysis of Vaccine-Related Sentiments on Twitter (X) from Development to Deployment of COVID-19 Vaccines
by Rohitash Chandra, Jayesh Sonawane and Jahnavi Lande
Big Data Cogn. Comput. 2024, 8(12), 186; https://doi.org/10.3390/bdcc8120186 - 13 Dec 2024
Cited by 1 | Viewed by 1602
Abstract
Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic caused fear and uncertainty about vaccines, which has been well expressed on social media platforms such as Twitter (X). We analyse sentiments from the [...] Read more.
Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic caused fear and uncertainty about vaccines, which has been well expressed on social media platforms such as Twitter (X). We analyse sentiments from the beginning of the COVID-19 pandemic and study the public behaviour on X during the planning, development, and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. We provide visualisation and analysis of anti-vaccine sentiments throughout the COVID-19 pandemic. We review the nature of the sentiments expressed with the number of tweets and monthly COVID-19 infections. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19. We also find that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised, implying that the vaccine rollout impacted the nature of discussions on social media. Full article
(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)
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