Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern |
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| Authors: | Xuefeng Wang Zhinan Wang Ying Huang Yuqin Liu Jiao Zhang Xiaofan Heng |
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| Institution: | 1. School of Management and Economics, Beijing Institute of Technology, Beijing, People’s Republic of China;2. Academy of Printing and Packaging Industrial Technology, Beijing Institute of Graphic Communication, Beijing, People’s Republic of China |
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| Abstract: | 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. |
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| Keywords: | Partner identification Subject-Action-Object semantic analysis term clumping correlation mapping problem & solution pattern dye-sensitized solar cells (DSSCs) |
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