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Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity

Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
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Author(s): Premisha Premananthan (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka), Banujan Kuhaneswaran (Sabaragamuwa University of Sri Lanka, Sri Lanka), Banage T. G. S. Kumara (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka)and Enoka P. Kudavidanage (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka)
Copyright: 2021
Pages: 19
Source title: Handbook of Research on Knowledge and Organization Systems in Library and Information Science
Source Author(s)/Editor(s): Barbara Jane Holland (Brooklyn Public Library, USA (retired) & Independent Researcher, USA)
DOI: 10.4018/978-1-7998-7258-0.ch009



Sri Lanka is one of the global biodiversity hotspots that contain a large variety of fauna and flora. But nowadays Sri Lankan wildlife has faced many issues because of poor management and policies to protect wildlife. The lack of technical and research support leads many researchers to retreat to select wildlife as their domain of study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. Then use those results to predict the trending topics of researches in the field of biodiversity. To model topics and extract the main keywords, the authors used the latent dirichlet allocation (LDA) algorithms. Using the topic modeling performance, an ontology model was also developed to describe the relationships between each keyword. They classified the research papers using the artificial neural network (ANN) using ontology instances to predict the future gaps for wildlife research papers. The automatic classification and labeling will lead many researchers to find their desired research papers accurately and quickly.

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