Semantics-based clustering approach for similar research area detection

Marion Olubunmi Adebiyi, Emmanuel B. Adigun, Roseline Oluwaseun Ogundokun, Abidemi Emmanuel Adeniyi, Peace Ayegba, Olufunke Oladipupo
2020 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
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
more » ... antics 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. Keywords: K-means clustering Latent semantic indexing Nigeria University Ontology-based preprocessing Semantics-based clustering This is an open access article under the CC BY-SA license.
doi:10.12928/telkomnika.v18i4.15001 fatcat:35gqessarngprozhjy3kyv2eoe