Issue Reorganization Using the Measure of Relevance
The need to extract R&D keywords from issues and use
them to retrieve R&D information is increasing rapidly. However, it is
difficult to identify related issues or distinguish them. Although the
similarity between issues cannot be identified, with an R&D lexicon,
issues that always share the same R&D keywords can be determined.
In detail, the R&D keywords that are associated with a particular issue
imply the key technology elements that are needed to solve a particular
issue.
Furthermore, the relationship among issues that share the same
R&D keywords can be shown in a more systematic way by clustering
them according to keywords. Thus, sharing R&D results and reusing
R&D technology can be facilitated. Indirectly, redundant investment
in R&D can be reduced as the relevant R&D information can be shared
among corresponding issues and the reusability of related R&D can be
improved. Therefore, a methodology to cluster issues from the
perspective of common R&D keywords is proposed to satisfy these
demands.
[1] R. Albright, "Taming text with the SVD," SAS Institute Inc., Jan. 2004.
[2] J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed.,
Morgan Kaufmann Publishers, 2011.
[3] I. H. Witten, "Text mining,” PracticalHandbook of Internet Computing,
CRC Press, 2004.
[4] I. Kim, "The Value of Big Data and Strategy," in 2012 Big Data Search
Analysis Technology Insight, 2012.
[5] J.Myung, D. Lee, and S. Lee., "A Korean Product Review Analysis
System Using a Semi-Automatically Constructed Semantic Dictionary,"
Journal of KIISE: Software and Applications, vol.35, pp. 392-403, 2008.
[6] B. Liu, Sentiment Analysis and Opinion Mining, Morgan & Claypool
Publishers, 2012.
[7] W. Fan, W. Wallace, S. Rich, and Z. Zhang, "Tapping the Power of Text
Mining,” Communications of the ACM, vol. 49, no. 9, pp. 76-82, 2006.
[8] F.Sebastisni, "Classification of Text,” Automatic, the Encyclopedia of
Language and Linguistics, 2nd ed., vol. 14, Elsevier Science Pub, 2006.
[9] A.Stanvrianou, P.Andritsos, and N.Nicoloyannis, "Overview and
Semantic Issues of Text Mining,” ACM SIGMOD Record, Vol. 36, pp.
23-24, 2007.
[10] Y. H. Kim, Social Network Analysis, Seoul, 2007.
[11] S.Kauffiman, The Origins of Order, Oxford University Press, 1993.
[12] S. Yoon, "A Study of Churn Prediction Model for Department Store
Customers Using Data Mining Technique," Asia Marketing Journal,
vol.6, no.4, pp. 45-72, 2005.
[13] C. Choi, "Research on Informal Organizational Network: Social Network
Analysis," Korea Society and Public Administration, vol.17, no.1, pp.
1-23, 2006.
[14] M. Kang, and Y. S. Hau, "Multi-level Analysis of the Antecedents of
Knowledge Transfer: Integration of Social Capital Theory and Social
Network Theory," Asia Pacific Journal of Information Systems, vol.22,
pp. 75-97, 2012.
[15] I. Cho, and N. Kim, "Recommending Core and Connecting Keywords of
Research Area Using Social Network and Data Mining Techniques,"
Journal of Intelligence and Information Systems, vol.17, pp. 127-138,
2011.
[16] Y. Hyun, H. Han, H. Choi, J. Park, K, Lee, K-Y. Kwahk, and N. Kim,
"Methodology Using Text Analysis for Packaging R&D Information
Services on Pending National Issues," Journal of Information Technology
Applications & Management, vol. 20, pp. 231-257, 2013.
[17] K. Y.Kwak, Social Network Analysis, Cheongram, Seoul, 2014.
[18] NodeXL: Network Overview, Discovery and Exploration for
Excel,Available at: http://nodexl.codeplex.com, Last accessed: 2nd
August 2014.
[19] UCINET: UCINET Software, Available at: https://sites.google.com/site/
ucinetsoftware/home, Last accessed: 2nd August 2014.
[20] NetMiner: NetMiner–Premier Software for Social Network
Analysis,Available at: http://www.netminer.com, Last accessed: 2nd
August 2014.
[21] S. Hong, Social Network World and Big Data Applications, Powerbook,
Seoul, 2013, pp. 235-238.
[22] NAVER News,Available at: http://news.naver.com, Last accessed: 2nd
August 2014.
[23] WIPSON,Available at: http://www.wipson.com, Last accessed: 2nd
August 2014.
[24] SAS Software, Available at: http://www.sas.com, Last accessed: 2nd
August 2014.
[1] R. Albright, "Taming text with the SVD," SAS Institute Inc., Jan. 2004.
[2] J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed.,
Morgan Kaufmann Publishers, 2011.
[3] I. H. Witten, "Text mining,” PracticalHandbook of Internet Computing,
CRC Press, 2004.
[4] I. Kim, "The Value of Big Data and Strategy," in 2012 Big Data Search
Analysis Technology Insight, 2012.
[5] J.Myung, D. Lee, and S. Lee., "A Korean Product Review Analysis
System Using a Semi-Automatically Constructed Semantic Dictionary,"
Journal of KIISE: Software and Applications, vol.35, pp. 392-403, 2008.
[6] B. Liu, Sentiment Analysis and Opinion Mining, Morgan & Claypool
Publishers, 2012.
[7] W. Fan, W. Wallace, S. Rich, and Z. Zhang, "Tapping the Power of Text
Mining,” Communications of the ACM, vol. 49, no. 9, pp. 76-82, 2006.
[8] F.Sebastisni, "Classification of Text,” Automatic, the Encyclopedia of
Language and Linguistics, 2nd ed., vol. 14, Elsevier Science Pub, 2006.
[9] A.Stanvrianou, P.Andritsos, and N.Nicoloyannis, "Overview and
Semantic Issues of Text Mining,” ACM SIGMOD Record, Vol. 36, pp.
23-24, 2007.
[10] Y. H. Kim, Social Network Analysis, Seoul, 2007.
[11] S.Kauffiman, The Origins of Order, Oxford University Press, 1993.
[12] S. Yoon, "A Study of Churn Prediction Model for Department Store
Customers Using Data Mining Technique," Asia Marketing Journal,
vol.6, no.4, pp. 45-72, 2005.
[13] C. Choi, "Research on Informal Organizational Network: Social Network
Analysis," Korea Society and Public Administration, vol.17, no.1, pp.
1-23, 2006.
[14] M. Kang, and Y. S. Hau, "Multi-level Analysis of the Antecedents of
Knowledge Transfer: Integration of Social Capital Theory and Social
Network Theory," Asia Pacific Journal of Information Systems, vol.22,
pp. 75-97, 2012.
[15] I. Cho, and N. Kim, "Recommending Core and Connecting Keywords of
Research Area Using Social Network and Data Mining Techniques,"
Journal of Intelligence and Information Systems, vol.17, pp. 127-138,
2011.
[16] Y. Hyun, H. Han, H. Choi, J. Park, K, Lee, K-Y. Kwahk, and N. Kim,
"Methodology Using Text Analysis for Packaging R&D Information
Services on Pending National Issues," Journal of Information Technology
Applications & Management, vol. 20, pp. 231-257, 2013.
[17] K. Y.Kwak, Social Network Analysis, Cheongram, Seoul, 2014.
[18] NodeXL: Network Overview, Discovery and Exploration for
Excel,Available at: http://nodexl.codeplex.com, Last accessed: 2nd
August 2014.
[19] UCINET: UCINET Software, Available at: https://sites.google.com/site/
ucinetsoftware/home, Last accessed: 2nd August 2014.
[20] NetMiner: NetMiner–Premier Software for Social Network
Analysis,Available at: http://www.netminer.com, Last accessed: 2nd
August 2014.
[21] S. Hong, Social Network World and Big Data Applications, Powerbook,
Seoul, 2013, pp. 235-238.
[22] NAVER News,Available at: http://news.naver.com, Last accessed: 2nd
August 2014.
[23] WIPSON,Available at: http://www.wipson.com, Last accessed: 2nd
August 2014.
[24] SAS Software, Available at: http://www.sas.com, Last accessed: 2nd
August 2014.
@article{"International Journal of Information, Control and Computer Sciences:68025", author = "William Wong Xiu Shun and Yoonjin Hyun and Mingyu Kim and Seongi Choi and Namgyu Kim", title = "Issue Reorganization Using the Measure of Relevance", abstract = "The need to extract R&D keywords from issues and use
them to retrieve R&D information is increasing rapidly. However, it is
difficult to identify related issues or distinguish them. Although the
similarity between issues cannot be identified, with an R&D lexicon,
issues that always share the same R&D keywords can be determined.
In detail, the R&D keywords that are associated with a particular issue
imply the key technology elements that are needed to solve a particular
issue.
Furthermore, the relationship among issues that share the same
R&D keywords can be shown in a more systematic way by clustering
them according to keywords. Thus, sharing R&D results and reusing
R&D technology can be facilitated. Indirectly, redundant investment
in R&D can be reduced as the relevant R&D information can be shared
among corresponding issues and the reusability of related R&D can be
improved. Therefore, a methodology to cluster issues from the
perspective of common R&D keywords is proposed to satisfy these
demands.
", keywords = "Clustering, Social Network Analysis, Text Mining,
Topic Analysis.", volume = "8", number = "10", pages = "1772-7", }
