Exploiting ontology recommendation using text categorization approach

Sarwar, Muhammad Azeem, Ahmed, Mansoor, Habib, Asad, Khalid, Muhammad, Akhtar Ali, M., Raza, Mohsin, Hussain, Shahid and Ahmed, Ghufran (2021) Exploiting ontology recommendation using text categorization approach. IEEE Access, 9. pp. 27304-27322. ISSN 2169-3536

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Abstract

Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.

Item Type:	Article
Uncontrolled Keywords:	clustering,ontology,recommendation system,semantic web,text categorization,text mining,unsupervised learning,computer science(all),materials science(all),engineering(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	17 Jun 2025 16:30
Last Modified:	17 Jun 2025 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99585
DOI:	10.1109/ACCESS.2020.3047364

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