Textual Data Mining to Support Science and Technology Management
Journal of Intelligent Information Systems
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Asymmetric information distances for automated taxonomy construction
Knowledge and Information Systems
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This paper presents a novel framework for supporting the development of well-informed research policies and plans. The proposed methodology is based on the use of bibliometrics; i.e., analysis is conducted using information regarding trends and patterns of publication. While using bibliometric techniques in this way is not a new idea, the proposed approach extends previous studies in a number of important ways. Firstly, instead of being purely exploratory, the focus of our research has been on developing techniques for detecting technologies that are in the early growth phase, characterized by a rapid increase in the number of relevant publications. Secondly, to increase the reliability of the forecasting effort, we propose the use of automatically generated keyword taxonomies, allowing the growth potentials of subordinate technologies to be aggregated into the overall potential of larger technology categories. A proof-of-concept implementation of each component of the framework is presented, and is used to study the domain of renewable energy technologies. Results from this analysis are presented and discussed.