An Integrated Decision Support Model to Assess Reviewers for Research Project Selection

Xu, Wei; Du, Wei; Ma, Jian; Wang, Wei; Liu, Qian

doi:10.1109/hicss.2012.103

Cited by 6 publications

(1 citation statement)

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“…To support specific phases for research project selection, a hybrid knowledge rule and genetic algorithm is suggested for research proposal grouping [10][11], and furthermore, an ontology based text mining method (OTMM) to cluster proposals is proposed for research project selection [12]. A group decision support method is offered to evaluate experts for research project selection [13], and an integrated decision support model to assess reviewers for research project selection [14]. A hybrid knowledge-based and modeling approach is offered to assign reviewers to proposals for research project selection [15], while a decision support approach is presented for assigning reviewers to grouped proposals, and genetic algorithm is employed to solve the assignment problem [16].…”

Section: Introductionmentioning

confidence: 99%

An Ontology Based Frequent Itemset Method to Support Research Proposal Grouping for Research Project Selection

2013

2013 46th Hawaii International Conference on System Sciences

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Research proposal grouping is one of the most important tasks for research project selection in research funding agencies. In this paper, a novel ontology based frequent itemset method is proposed to deal with research proposal grouping problem. In the proposed method, a research ontology is firstly constructed to standardize research keywords. Secondly, frequent itemsets with different support degrees are extracted from research proposals based on research ontology. Thirdly, a new measure of similarity degree between two research proposals is developed and then a clustering algorithm is proposed to classify research proposals based on the similarity degree, in which some parameters are discussed, and the proper parameters are selected. Finally, when the number of research proposals in some clusters is still large, research proposals are further divided into small groups, in which the number of research proposals is approximately equal. The proposed method is validated based on the selection process at the National Natural Science Foundation of China (NSFC). The experimental results show that our proposed method can improve the efficiency and effectiveness of research proposal grouping, and is a potential and alternative one to support research project selection processes in other governments and private research funding agencies.

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