Analyzing Sci-Tech Topics Based on Semantic Representation of Patent References
Jinzhu Zhang1,2(),Yue Wang1,Yiming Hu1
1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2 Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China
[Objective] This paper explores the content mining method for scientific references in patent (SRP) based on text semantic representation. It also improves the accuracy, comprehensiveness and interpretability of knowledge flow analysis. [Methods] Firstly, we extracted keywords and abstracts from patents to represent the SRPs and created vectors for these items. Then, we computed the distance between vectors to calculate their semantic similarities. Finally, we obtained and mapped the topics of patents and SRP contents from the field of nanotechnology. [Results] We found our method could map relationship among sci-tech topics from the content perspective effectively. [Limitations] We only conducted exploratory research with abstracts and keywords rather than full texts. [Conclusions] The proposed method improves the knowledge flow analysis of patents.
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