Combining SAO semantic analysis and morphology analysis to identify technology opportunities

Xuefeng Wang (), Pingping Ma, Ying Huang, Junfang Guo, Donghua Zhu, Alan L. Porter and Zhinan Wang
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Xuefeng Wang: Beijing Institute of Technology
Pingping Ma: Beijing Institute of Technology
Ying Huang: Beijing Institute of Technology
Junfang Guo: Beijing Institute of Technology
Donghua Zhu: Beijing Institute of Technology
Alan L. Porter: Georgia Institute of Technology
Zhinan Wang: Beijing Institute of Technology

Scientometrics, 2017, vol. 111, issue 1, No 2, 3-24

Abstract: Abstract Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject–action–object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.

Keywords: Technology opportunities analysis; SAO semantic analysis; Morphological analysis; Technology mining; Dye-sensitized solar cells (DSSCs) (search for similar items in EconPapers)
JEL-codes: C80 O32 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s11192-017-2260-y

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