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- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
A Study on Developing a Prediction Model of Patent Citation Counts
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Abstract
The purpose of this study is to develop a prediction model of patent citation counts based on major factors which affect patent citation. To this end, we performed multiple regression analysis between the patent citation counts and five explanatory variables such as the number of pages, the number of claims, the reference-average-citation rate, the strength of bibliographic coupling, and the document similarity proved as having 5% or more standardized variances(r2) with patent citation counts, with a test dataset of U.S. patents in five subject fields. As a result, our prediction models showed 58.3% to 89.6% predictability depending on subject fields and revealed the document similarity has the highest impact on citation counts among the five predictive variables in all the subject fields. The result of comparison between the predicted citation counts and the actual ones confirmed the usefulness of the citation prediction models built for each subject field.
- keywords
- 인용분석, 인용예측, 특허인용, 인용예측모형, citation analysis, citation prediction, patent citation, citation prediction models, citation analysis, citation prediction, patent citation, citation prediction models
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