Exploiting Ontology Recommendation Using Text Categorization Approach

Muhammad Azeem Sarwar, Mansoor Ahmed*, Asad Habib, Muhammad Khalid, Akhtar Ali, Mohsin Raza, Shahid Hussain, Ghufran Ahmed

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)27304 - 27322
Number of pages19
JournalIEEE Access
Volume9
Early online date25 Dec 2020
DOIs
Publication statusPublished - 18 Feb 2021

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