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Leveraging cloud computing for the semantic web: review and trends

ADEDUGBE, Oluwasegun, BENKHELIFA, Elhadj, CAMPION, Russell, al-obeidat, feras, bani hani, anoud and Jayawickrama, Uchitha (2019) Leveraging cloud computing for the semantic web: review and trends. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 24. pp. 5999-6014. ISSN 1432-7643

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Official URL: https://doi.org/10.1007/s00500-019-04559-2

Abstract or description

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a “cloud-driven” interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications.

Item Type: Article
Additional Information: “This is a post-peer-review, pre-copyedit version of an article published in Soft Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00500-020-04816-9”
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Elhadj BENKHELIFA
Date Deposited: 06 Apr 2020 14:39
Last Modified: 24 Feb 2023 13:58
URI: https://eprints.staffs.ac.uk/id/eprint/6222

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