TY - JOUR
T1 - Subject-action-object-based morphology analysis for determining the direction of technological change
AU - Guo, Junfang
AU - Wang, Xuefeng
AU - Li, Qianrui
AU - Zhu, Donghua
N1 - Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - Morphology analysis, despite being a strong stimulus for the development of new alternatives, largely relies on domain experts and neglects the relationships between keywords in the construction of morphological structures. In addition, there are few systematic approaches to prioritize the morphological configurations. To address these issues, a hybrid approach is proposed, which enhances the performance of morphology analysis by combining it with subject-action-object (SAO) semantic analysis. Initially, a keyword co-occurrence patent set for subsequent SAO analysis is prepared based on keywords frequency vector analysis. Then, SAO structures are extracted and semantic analysis is performed to identify the relationships between keywords, which help to build morphological structures more objectively. In addition, a well-defined evaluation system that contains eight sub-indexes is proposed to evaluate the morphological configurations. Finally, to demonstrate and validate the proposed approach, the dye-sensitized solar cells technology is employed as the case study. Results indicate that the most promising combination we predict appears frequently in 2012-2014 and the distribution of it is also close to the fact in 2012-2014. Accordingly, the proposed method can be used to effectively determine the direction of technological change and to forecast technology innovation opportunities.
AB - Morphology analysis, despite being a strong stimulus for the development of new alternatives, largely relies on domain experts and neglects the relationships between keywords in the construction of morphological structures. In addition, there are few systematic approaches to prioritize the morphological configurations. To address these issues, a hybrid approach is proposed, which enhances the performance of morphology analysis by combining it with subject-action-object (SAO) semantic analysis. Initially, a keyword co-occurrence patent set for subsequent SAO analysis is prepared based on keywords frequency vector analysis. Then, SAO structures are extracted and semantic analysis is performed to identify the relationships between keywords, which help to build morphological structures more objectively. In addition, a well-defined evaluation system that contains eight sub-indexes is proposed to evaluate the morphological configurations. Finally, to demonstrate and validate the proposed approach, the dye-sensitized solar cells technology is employed as the case study. Results indicate that the most promising combination we predict appears frequently in 2012-2014 and the distribution of it is also close to the fact in 2012-2014. Accordingly, the proposed method can be used to effectively determine the direction of technological change and to forecast technology innovation opportunities.
KW - Dye-sensitized solar cells (DSSCs)
KW - Morphology analysis
KW - Subject-action-object (SAO)
KW - Technology change
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=84957671938&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2016.01.028
DO - 10.1016/j.techfore.2016.01.028
M3 - Article
AN - SCOPUS:84957671938
SN - 0040-1625
VL - 105
SP - 27
EP - 40
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
ER -