Automatic Labelling & Classification for Research Papers of Wildlife in Sri Lanka

Abstract

Sri Lanka, is one of the global biodiversity hotspots, which contains a larger variety of fauna and flora. But nowadays Sri Lankan wildlife faced many issues because of poor management and policies to protect wildlife. The lack of technical & research support leads many researchers to retreat to select wildlife as their domain of study. Wildlife research results should be integrated into data-driven decisions on conservation and management, but the existing contribution is not sufficient. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. To model topics and extract the main keywords, we 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. We 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. Our model provides 83% accuracy in this labeling and classification using past research papers on the wildlife of Sri Lanka