Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern (full-text)

Today’s companies still rely heavily on expert knowledge rather than quantitative data with a systematic approach to effectively identify and choose Research and Development (R&D) partners. It is advantageous to identify and select potential R&D partners using a Problem & Solution (P&S) pattern. This paper presents a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. First, we choose a thematic dataset that contains problems and quantitative data with relative topic terms. Then, we extract Subject-Action-Object semantic structures in a P&S pattern from the dataset, and identify various solutions to a technical problem, with each as a subject. In addition, we provide correlation mapping to visualise the text characters and identify R&D partners. Finally, we validate the proposed method through a case study of the dye-sensitized solar cells sector.

http://www.tandfonline.com/doi/abs/10.1080/09537325.2016.1277202?journalCode=ctas20

For full-text see https://www.researchgate.net/publication/312341963_Identifying_RD_partners_through_Subject-Action-Object_semantic_analysis_in_a_problem_solution_pattern

Author(s): Xuefeng Wang, Zhinan Wang, Ying Huang, Yuqin Liu, Jiao Zhang, Xiaofan Heng, Donghua Zhu
Organization(s): Beijing Institute of Technology
Source: Technology Analysis & Strategic Management
Year: 2017

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