"Term clumping" for technical intelligence: A case study on dye-sensitized solar cells

Zhang, Y; Porter, AL; Hu, Z; Guo, Y; Newman, NC

"Term clumping" for technical intelligence: A case study on dye-sensitized solar cells

Zhang, Y

Porter, AL

Hu, Z Guo, Y Newman, NC

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Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2014, 85 pp. 26 - 39
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Porter, AL https://orcid.org/0000-0002-4520-6518	en_US
dc.contributor.author	Hu, Z	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Newman, NC	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Technological Forecasting and Social Change, 2014, 85 pp. 26 - 39	en_US
dc.identifier.issn	0040-1625	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118849
dc.description.abstract	Tech Mining seeks to extract intelligence from Science, Technology & Innovation information record sets on a subject of interest. A key set of Tech Mining interests concerns which R&D activities are addressed in the publication and patent abstract records under study. This paper presents six "term clumping" steps that can clean and consolidate topical content in such text sources. It examines how each step changes the content, potentially to facilitate extraction of usable intelligence as the end goal. We illustrate for an emerging technology, dye-sensitized solar cells. In this case we were able to reduce some 90,980 terms & phrases to more user-friendly sets through the clumping steps as one indicator of success. The resulting phrases are better suited to contributing usable technical intelligence than the original results. We engaged seven persons knowledgeable about dye-sensitized solar cells (DSSCs) to assess the resulting content. These empirical results advanced the development of a semi-automated term clumping process that can enable extraction of topical content intelligence. © 2014 Elsevier Inc.	en_US
dc.relation.ispartof	Technological Forecasting and Social Change	en_US
dc.relation.isbasedon	10.1016/j.techfore.2013.12.019	en_US
dc.subject.classification	Science Studies	en_US
dc.title	"Term clumping" for technical intelligence: A case study on dye-sensitized solar cells	en_US
dc.type	Journal Article
utslib.citation.volume	85	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	10 Technology	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	85	en_US

Abstract:

Tech Mining seeks to extract intelligence from Science, Technology & Innovation information record sets on a subject of interest. A key set of Tech Mining interests concerns which R&D activities are addressed in the publication and patent abstract records under study. This paper presents six "term clumping" steps that can clean and consolidate topical content in such text sources. It examines how each step changes the content, potentially to facilitate extraction of usable intelligence as the end goal. We illustrate for an emerging technology, dye-sensitized solar cells. In this case we were able to reduce some 90,980 terms & phrases to more user-friendly sets through the clumping steps as one indicator of success. The resulting phrases are better suited to contributing usable technical intelligence than the original results. We engaged seven persons knowledgeable about dye-sensitized solar cells (DSSCs) to assess the resulting content. These empirical results advanced the development of a semi-automated term clumping process that can enable extraction of topical content intelligence. © 2014 Elsevier Inc.

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http://hdl.handle.net/10453/118849