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Semantics-based clustering approach for similar research area detection

Adebiyi, Marion O. and ADIGUN, EMMANUEL BUKUNMI and Ogundokun, Roseline Oluwaseun and Adeniyi, B.A and Ayegba, Peace and Oladipupo, O. O. (2020) Semantics-based clustering approach for similar research area detection. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18 (4). pp. 1874-1883. ISSN 1693-6930

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The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results.

Item Type: Article
Uncontrolled Keywords: K-means clustering Latent semantic indexing Nigeria University Ontology-based preprocessing Semantics-based clustering
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Dr ibukun Afolabi
Date Deposited: 18 Jun 2021 15:50
Last Modified: 18 Jun 2021 15:50

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