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

Author

Listed:
  • Zhang, Yi
  • Porter, Alan L.
  • Hu, Zhengyin
  • Guo, Ying
  • Newman, Nils C.

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.

Suggested Citation

  • Zhang, Yi & Porter, Alan L. & Hu, Zhengyin & Guo, Ying & Newman, Nils C., 2014. "“Term clumping” for technical intelligence: A case study on dye-sensitized solar cells," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 26-39.
    • Handle: RePEc:eee:tefoso:v:85:y:2014:i:c:p:26-39
    DOI: 10.1016/j.techfore.2013.12.019
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    2. Abraham Bookstein & Vladimir Kulyukin & Timo Raita & John Nicholson, 2003. "Adapting measures of clumping strength to assess term‐term similarity," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(7), pages 611-620, May.
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