TELKOMNIKA Telecommunication, Computing, Electronics and Control
Semantics-based clustering approach for similar research area detection

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Semantics-based clustering approach for similar research area detection

K-means clustering, Latent semantic indexing, Nigeria University, Ontology-based preprocessing, Semantics-based clustering

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.

Marion Oluwabunmi Adebiyi, Emmanuel B. Adigun, Roseline Oluwaseun Ogundokun, Abidemi Emmanuel Adeniyi, Peace Ayegba, Olufunke O. Oladipupo

DOI: 10.12928/TELKOMNIKA.v18i4.15001

Universitas Ahmad Dahlan

August 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control