Abstract
The NSFC is the largest government funding agency in China, with the primary aim to fund and manage basic research. The agency is made up of seven scientific departments, four bureaus, one general office, and three associated units. The scientific departments are the decision-making units responsible for funding recommendations and management of funded projects. Selection of research projects is an important and recurring activity in many organizations such as government research funding agencies. Current method of grouping proposals are based on manual matching of similar research discipline areas but it fails to be accurate. Text clustering methods those are not having semantic approach provide less accuracy. A novel ontology based text mining approach to cluster proposals is proposed. Research project selection is an important task for government and private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. The grouped proposals are then assigned to the appropriate experts for peer review. The review results are collected, and the proposals are then ranked based on the aggregation of the experts' review results.
Published Version
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