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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

Premisha Premananthan, Banujan Kuhaneswaran, Banage T. G. S. Kumara, Enoka P. Kudavidanage
Copyright: © 2021 |Pages: 19
ISBN13: 9781799872580|ISBN10: 1799872580|EISBN13: 9781799872597
DOI: 10.4018/978-1-7998-7258-0.ch009
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MLA

Premananthan, Premisha, et al. "Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity." Handbook of Research on Knowledge and Organization Systems in Library and Information Science, edited by Barbara Jane Holland, IGI Global, 2021, pp. 157-175. https://doi.org/10.4018/978-1-7998-7258-0.ch009

APA

Premananthan, P., Kuhaneswaran, B., Kumara, B. T., & Kudavidanage, E. P. (2021). Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity. In B. Holland (Ed.), Handbook of Research on Knowledge and Organization Systems in Library and Information Science (pp. 157-175). IGI Global. https://doi.org/10.4018/978-1-7998-7258-0.ch009

Chicago

Premananthan, Premisha, et al. "Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity." In Handbook of Research on Knowledge and Organization Systems in Library and Information Science, edited by Barbara Jane Holland, 157-175. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7258-0.ch009

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Abstract

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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