Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

Taeyeoun Roh, Yujin Jeong and Byungun Yoon
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Taeyeoun Roh: Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea
Yujin Jeong: Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea
Byungun Yoon: Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea

Sustainability, 2017, vol. 9, issue 11, 1-19

Abstract: Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since keyword-based text mining has the limitation whereby, when screening useful keywords, it frequently omits meaningful keywords, analyzers therefore need to repeat the careful scrutiny of the derived keywords to clarify the meaning of keywords. In this research, we structure meaningful keyword sets related to technological information from patent documents; then we layer the keywords, depending on the level of information. This research involves two steps. First, the characteristics of technological information are analyzed by reviewing the patent law and investigating the description of patent documents. Second, the technological information is structured by considering the information types, and the keywords in each type are layered through natural language processing. Consequently, the structured and layered keyword set does not omit useful keywords and the analyzer can easily understand the meaning of each keyword.

Keywords: text mining; NLP; technological information; patent analysis; text structure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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