In this thesis, a framework based on text mining techniques is proposed to discover useful intelligence implicit in large bodies of electronic text sources. This intelligence is a prime requirement for successful R&D management. This research extends the approach called “Technology Opportunities Analysis” (developed by the Technology Policy and Assessment Center, Georgia Institute of Technology, in conjunction with Search Technology, Inc.) to create the proposed framework. The commercialized software, called VantagePoint, is mainly used to perform basic analyses. In addition to utilizing functions in VantagePoint, this thesis also implements a novel text association rule mining algorithm for gathering related concepts among text data. Two algorithms based on text association rule mining are also implemented. The first algorithm called “tree-structured networks” is used to capture important aspects of both parent-child (hierarchical structure) and sibling relations (non-hierarchical structure) among related terms. The second algorithm called “concept-grouping” is used to construct term thesauri for data preprocessing. Finally, the framework is applied to Thai xvi S&T publication abstracts toward the objective of improving R&D management. The results of the study can help support strategic decision-making on the direction of S&T programs in Thailand.
Doctoral candidate: Alisa Kongthon
Committee: Alan L. Porter, Xiaoming Huo, Donghua Zhu, Jye-Chyi Lu, and Susan E. Cozzens
University: Georgia Tech
Degree program: Doctor of Philosophy in Industrial Engineering
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