TY - JOUR
T1 - An ontology-based text-mining method to cluster proposals for research project selection
AU - Ma, Jian
AU - Xu, Wei
AU - Sun, Yong Hong
AU - Turban, Efraim
AU - Wang, Shouyang
AU - Liu, Ou
N1 - Funding Information:
Manuscript received October 13, 2009; revised June 22, 2010 and January 14, 2011; accepted May 31, 2011. Date of publication March 19, 2012; date of current version April 13, 2012. This work was supported in part by the National Natural Science Foundation of China (Project No. 71001103, 71171172, and J1124003) and in part by the General Research Fund of the Hong Kong SAR Government (Project No: 119611). This paper was recommended by Associate Editor W. Pedrycz.
Funding Information:
Fig. 1 shows the processes of research project selection at the National Natural Science Foundation of China (NSFC), i.e., CFP, proposal submission, proposal grouping, proposal assignment to experts, peer review, aggregation of review results, panel evaluation, and final awarding decision [1]. These processes are very similar in other funding agencies, except that there are a very large number of proposals that need to be grouped for peer review in the NSFC.
PY - 2012/5
Y1 - 2012/5
N2 - 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. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the proposals' full text can be used effectively. Text-mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. However, these methods have limitations when dealing with non-English language texts, e.g., Chinese research proposals. This paper presents a novel ontology-based text-mining approach to cluster research proposals based on their similarities in research areas. The method is efficient and effective for clustering research proposals with both English and Chinese texts. The method also includes an optimization model that considers applicants' characteristics for balancing proposals by geographical regions. The proposed method is tested and validated based on the selection process at the National Natural Science Foundation of China. The results can also be used to improve the efficiency and effectiveness of research project selection processes in other government and private research funding agencies.
AB - 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. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the proposals' full text can be used effectively. Text-mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. However, these methods have limitations when dealing with non-English language texts, e.g., Chinese research proposals. This paper presents a novel ontology-based text-mining approach to cluster research proposals based on their similarities in research areas. The method is efficient and effective for clustering research proposals with both English and Chinese texts. The method also includes an optimization model that considers applicants' characteristics for balancing proposals by geographical regions. The proposed method is tested and validated based on the selection process at the National Natural Science Foundation of China. The results can also be used to improve the efficiency and effectiveness of research project selection processes in other government and private research funding agencies.
KW - Clustering analysis
KW - decision support systems
KW - ontology
KW - research project selection
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=84862817609&partnerID=8YFLogxK
U2 - 10.1109/TSMCA.2011.2172205
DO - 10.1109/TSMCA.2011.2172205
M3 - Article
AN - SCOPUS:84862817609
SN - 1083-4427
VL - 42
SP - 784
EP - 790
JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
IS - 3
M1 - 6171866
ER -