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Article

Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling

by
Georgiana Stănescu (Nicolaie)
and
Simona-Vasilica Oprea
*
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(7), 1313; https://doi.org/10.3390/electronics14071313
Submission received: 28 February 2025 / Revised: 21 March 2025 / Accepted: 25 March 2025 / Published: 26 March 2025

Abstract

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

Share and Cite

MDPI and ACS Style

Stănescu, G.; Oprea, S.-V. Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling. Electronics 2025, 14, 1313. https://doi.org/10.3390/electronics14071313

AMA Style

Stănescu G, Oprea S-V. Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling. Electronics. 2025; 14(7):1313. https://doi.org/10.3390/electronics14071313

Chicago/Turabian Style

Stănescu (Nicolaie), Georgiana, and Simona-Vasilica Oprea. 2025. "Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling" Electronics 14, no. 7: 1313. https://doi.org/10.3390/electronics14071313

APA Style

Stănescu, G., & Oprea, S.-V. (2025). Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling. Electronics, 14(7), 1313. https://doi.org/10.3390/electronics14071313

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