Recent Trends And Insights In Semantic Web And Ontology Driven Knowledge Representation Across Disciplines Using Topic Modeling

Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling.pdf (Private)

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

Terms

• SWT: Semantic Web Technologies.
• Bibliometrics: Statistical analysis of scientific publications and citation data.
• BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding (Private): Bidirectional Encoder Representations from Transformers.
• Latent Dirichlet Allocation (LDA): Probabilistic topic-modeling algorithm that discovers latent topics within a collection of documents.
• Semantic interoperability: Ability of systems to exchange data while preserving shared meaning.

Intro

Survey of 10,037 Web of Science publications (2019–2024) examining trends in Semantic Web technologies across multiple disciplines. Focuses on:

1. Cross-domain integration of Semantic Web technologies.
2. Bibliometric and NLP-driven trend analysis.
3. Graph-based visualization of research relationships.
4. Topic modeling using LDA and BERT embeddings. Research questions:

• How have ontologies and Semantic Web technologies evolved across disciplines?
• What associations exist between ontology-driven knowledge representation and semantic technologies?
• What latent research topics emerge from the literature?

Relevant to my research

• Ontologies remain the primary mechanism for semantic interoperability between heterogeneous datasets.
• Knowledge graphs and linked data are repeatedly identified as key technologies for integrating distributed scientific data.
• AI and ML are increasingly combined with ontologies to improve semantic search, reasoning, and automated knowledge discovery.
• Bioinformatics is one of the dominant application areas, alongside computer science and engineering.
• Dynamic ontology updates, scalability, and interoperability remain major open research challenges.

Main topics identified

1. Ontology-driven knowledge representation and intelligent systems.
2. Bioinformatics, gene expression, and biological data analysis.
3. Systems biology, advanced bioinformatics, and ethical/legal considerations. overall:

• Semantic Web research is becoming increasingly interdisciplinary.
• Bioinformatics is one of the strongest adopters of ontologies and knowledge graphs.
• Current research is shifting from simply representing knowledge toward integrating Semantic Web technologies with AI and machine learning.
• Linked data and semantic interoperability remain central goals across nearly every application domain.

Conclusion

The paper reinforces that ontologies, linked data, and knowledge graphs are becoming core infrastructure for integrating heterogeneous scientific datasets. It also identifies AI integration, scalable ontologies, and semantic interoperability as major future directions, all of which align closely with Linked Open Data initiatives in bioinformatics.