Science, Technology & Innovation Textual Data – Oriented Topic Analysis and Forecasting: Model and A Case Study

Zhang, Y; Zhang, G; Porter, A; Zhu, D; Lu, J

Science, Technology & Innovation Textual Data – Oriented Topic Analysis and Forecasting: Model and A Case Study

Zhang, Y

Zhang, G

Porter, A Zhu, D Lu, J

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Publication Type:: Conference Proceeding
Citation:: 2014
Issue Date:: 2014-11-27

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Porter, A	en_US
dc.contributor.author	Zhu, D	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date	2014-11-27	en_US
dc.date.issued	2014-11-27	en_US
dc.identifier.citation	2014	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35436
dc.description.abstract	Not only the external quantities, but also the potential topics of current Science, Technology & Innovation (ST&I) are changing all the time, and their induced accumulative innovation or, even, disruptive revolution, should be able to heavily influence the whole society in the near future. Addressing and predicting the changes, this paper proposes an analytic method (1) to cluster associated terms and phrases to constitute meaningful technological topics and (2) to identify changing topical emphases, the results of which we carry forward to present mechanisms to forecast prospective developments via Technology Roadmapping approaches. Furthermore, an empirical case study of Award data in the United States National Science Foundation Division of Computer and Communication Foundations is performed to demonstrate the proposed method and the resulting knowledge could hold interests for R&D management and science policy in practice.	en_US
dc.relation.ispartof	5th International Conference on Future-Oriented Technology Analysis (FTA)	en_US
dc.title	Science, Technology & Innovation Textual Data – Oriented Topic Analysis and Forecasting: Model and A Case Study	en_US
dc.type	Conference Proceeding
utslib.location.activity	Brussels	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Brussels
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2014-11-28	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2014-11-27	en_US

Abstract:

Not only the external quantities, but also the potential topics of current Science, Technology & Innovation (ST&I) are changing all the time, and their induced accumulative innovation or, even, disruptive revolution, should be able to heavily influence the whole society in the near future. Addressing and predicting the changes, this paper proposes an analytic method (1) to cluster associated terms and phrases to constitute meaningful technological topics and (2) to identify changing topical emphases, the results of which we carry forward to present mechanisms to forecast prospective developments via Technology Roadmapping approaches. Furthermore, an empirical case study of Award data in the United States National Science Foundation Division of Computer and Communication Foundations is performed to demonstrate the proposed method and the resulting knowledge could hold interests for R&D management and science policy in practice.

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http://hdl.handle.net/10453/35436