Introduction
The main focus of this research is founded on the topic of ontologies and the semantic web. Ontologies are essential components in Computer Science since they act as conceptual frameworks for ensuring the availability of domain knowledge across information systems. Primarily, ontologies play a vital role in conceptualizing the semantic web, whereby they facilitate necessary semantic knowledge utilized for annotating websites in a relevant manner for machine interpretation (Patel & Jain, 2019; Rhayem, Mhiri & Gargouri, 2020). Various studies in web research associate ontologies with taxonomies, specific vocabularies, classification schemes, and dictionaries to describe their nature of existence. Resource Description Framework (RDF) and Web Ontology Language (OWL) are the most used languages in semantic web services to interpret the content of files, coupled with customizable XML (Martinez-Rodriguez, Hogan & Lopez-Arevalo, 2020; Mishra & Jain, 2018; Wimmer, Chen & Narock, 2018). Software agents can automatically develop contemporary services and applications from previously published descriptions through these knowledge models. This research surveys the current domain of ontologies and related enabling components of the semantic web, focusing on RDF and OWL descriptive languages.
Rationale
The primary purpose of semantic web research is to allow the practical application of web-based information and components by applications and network systems. The World Wide Web Consortium (W3C) has created standard formats, particularly RDF and OWL, for efficient description of web files and data (d’Amato, 2020; Hitzler, 2021; Qassimi & Abdelwahed, 2019). Recent developments of the World Wide Web have led to numerous sources of information that are not easily comprehensible. As a result, advanced techniques are urgently required to assist software agents and applications in making sense of the information through filtering and relationship development (Qassimi & Abdelwahed, 2019). The rising number of knowledge workers in low- and middle-income nations necessitates further research on the selected topic to promote understanding metadata and ontologies to accomplish machine interpretability. In this case, this research seeks to perform a comprehensive systematic review of available literature regarding interpretable machine illustrations to contribute to the development of more intelligent software systems, which can automatically learn and create new services based on web-related information.
Research Objectives
The general objective of this study is to examine semantic web technologies, specifically ontologies, which specify the semantics and descriptions of web resources. This research focuses on the following specific objectives:
- Elaborate on the integration of metadata schemes and ontologies to ensure machine interpretability.
- Discuss the evolution of semantic web technologies.
- Provide a comprehensive illustration of recent descriptive languages: RDF and OWL.
- Understand the role of ontologies in defining the semantics of web applications and resources.
Methodology
This quantitative research study employs a systematic literature review of recently published articles to explore the topic of ontologies and the semantic web and establish the related issues. An inclusion and exclusion criteria of the research process will be integrated based on the language, topic variables, and period of publishing. Various databases such as IEEE, ScienceDirect, Emerald, Springer, and Google Scholar will be utilized to obtain English-related studies. The research will focus on articles published during the past five years. The application of the systematic review strategy seeks to provide a conceptual understanding of the semantic web environment and ontologies as the knowledge domain.
Conclusion
This semantic web research aims to establish the key technologies utilized to develop software systems for ontology learning and the generation of interpretable web-related information. Ontologies basically entail semantic understanding through the utilization of conceptualized knowledge domains. As such, a comprehensive systematic review on current ontological frameworks in the semantics web research will contribute to a better understanding concerning the processes of machine interpretability.
References
d’Amato, C. (2020). Machine Learning for the Semantic Web: Lessons learnt and next research directions. Semantic Web, 11(1), 195-203. doi: 10.3233/sw-200388
Hitzler, P. (2021). A review of the semantic web field. Communications Of The ACM, 64(2), 76-83. doi: 10.1145/3397512
Martinez-Rodriguez, J., Hogan, A., & Lopez-Arevalo, I. (2020). Information extraction meets the Semantic Web: A survey. Semantic Web, 11(2), 255-335. doi: 10.3233/sw-180333
Mishra, S., & Jain, S. (2018). Ontologies as a semantic model in IoT. International Journal of Computers and Applications, 42(3), 233-243. doi: 10.1080/1206212x.2018.1504461
Patel, A., & Jain, S. (2019). Present and future of semantic web technologies: a research statement. International Journal of Computers and Applications, 43(5), 413-422. doi: 10.1080/1206212x.2019.1570666
Rhayem, A., Mhiri, M., & Gargouri, F. (2020). Semantic Web Technologies for the Internet of Things: Systematic Literature Review. Internet Of Things, 11, 100206. doi: 10.1016/j.iot.2020.100206
Qassimi, S., & Abdelwahed, E. (2019). The role of collaborative tagging and ontologies in emerging semantic of web resources. Computing, 101(10), 1489-1511. doi: 10.1007/s00607-019-00704-9
Wimmer, H., Chen, L., & Narock, T. (2018). Ontologies and the Semantic Web for Digital Investigation Tool Selection. Journal of Digital Forensics, Security and Law. doi: 10.15394/jdfsl.2018.1569
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