• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Practical Text Mining for Trend Analysis: Ontology to visualization in Aerospace Technology

Vol. 11, No.8, August 31, 2017
10.3837/tiis.2017.08.022, Download Paper (Free):

Abstract

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.


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Cite this article

[IEEE Style]
Yoosin Kim, Yeonjin Ju, SeongGwan Hong and Seung Ryul Jeong, "Practical Text Mining for Trend Analysis: Ontology to visualization in Aerospace Technology," KSII Transactions on Internet and Information Systems, vol. 11, no. 8, pp. 4133-4145, 2017. DOI: 10.3837/tiis.2017.08.022

[ACM Style]
Kim, Y., Ju, Y., Hong, S., and Jeong, S. R. 2017. Practical Text Mining for Trend Analysis: Ontology to visualization in Aerospace Technology. KSII Transactions on Internet and Information Systems, 11, 8, (2017), 4133-4145. DOI: 10.3837/tiis.2017.08.022