Current Trends of Designing E-Governance Ontology for Cloud-Based Smart and Trusted Citizen-Centric Services: A Systematic Literature Review

Abstract

E-governance practices have been transforming citizen-government interactions, enhancing transparency, accessibility, and efficiency in numerous countries. Since each country has its own governance mechanisms, government culture, and systems, a single model framework or ontology cannot be optimally adapted. Current E-governance ontologies, frameworks, and models lack the necessary contextualised adaptability, interoperability, and tailor-made personalisation to fit in the country-specific transformation needs of E-governance, i.e., delivering smart, intelligent, and trusted citizen-centric services. This study presents a summary of key findings from 49 peer-reviewed repositories selected from Web of Science, Scopus, ScienceDirect, Google Scholar, IEEE Xplore, Springer, PubMed, and the ACM Library in terms of research gaps in available features, methodologies, tools and techniques in the cloud-based e-governance models, frameworks, and ontologies. It aims to formulate a problem statement to navigate and guide the futuristic research solutions in the domain. Research articles were screened using the PRISMA 2020 statement guidelines based on significant filtration and selection criteria and then systematically reviewed. Technically, these identified research gaps were listed as 28% of semantic interoperability; 11% noted legal and policy barriers; 15% focused on lack of awareness; 21% mentioned lack of common semantic languages, lack of advanced smart features, and linguistic localization; and 25% notified lack of intra-governance integration for citizen service collaborations. This can help in improving the government’s citizen-centric service using the next-generation E-Governance over the Cloud. Another observation indicated that the majority of the reviewed articles were focused on the semantic integration of data and interoperability. Hence, it was concluded based on those research articles that the lack of semantic integration and interoperability issues (28%) are the main challenges for designing the cloud-based e-governance ontology for smart, intelligent, and trustworthy citizen-centric services. Future studies should focus on developing specific modeling methods for ontology entities, fully implementing and validating proposed solutions in real-world scenarios, and continuing to explore the scalability and adaptability of these architectures to handle growing data volumes and evolving user or citizen demands efficiently.

Keywords: E-governance Ontology, Adaptive Solution, Smart System, Citizen-Centric, Cloud Computing, Intelligent Systems