| 基于专利技术功效主题词与专利引文共现的核心专利挖掘 |
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| 引用本文： | 许海云,岳増慧,雷炳旭,方曙.基于专利技术功效主题词与专利引文共现的核心专利挖掘[J].图书情报工作,2014,58(4):59-64. |
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| 作者姓名： | 许海云 岳増慧 雷炳旭 方曙 |
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| 作者单位： | 1. 中国科学院国家科学图书馆成都分馆;2. 中国科学院大学 |
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| 基金项目： | 本文系中国科学院西部之光-西部博士资助项目“基于内容分析的领域核心专利挖掘及专利权人竞合态势分析方法研究”（项目编号：Y4C0011001）研究成果之一。 |
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| 摘 要： | 认为利用专利技术功效主题词与专利引文进行共现分析可直观识别核心专利簇的技术功效特征。与专利引文共现分析相比，专利技术功效主题词与专利引文的交叉共现增加了主题标识，使得基于相同标引主题词的被引专利有相似的技术主题和功效特征。同时，不同的技术功效主题词通过专利引文交叉共现，可以识别不同技术主题的关联度以及技术主题对应的功效特征。最后，选择医用显微内窥镜领域相关专利做实证分析，通过技术功效主题词-专利引文、技术主题词-专利引文以及专利引文共现分析，论证专利技术功效主题词与专利引文共现方法在核心专利挖掘方面的特征、优越性及不足。
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| 关 键 词： | 主题挖掘 引文分析 共现分析 技术功效 矩阵 |
| 收稿时间： | 2013-12-19 |
| 修稿时间： | 2014-01-27 |
Core Patents Mining based on Cross Co-occurrence Analysis to Patent Technology-Effect Subject Terms and Citations |
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| Xu Haiyun,Yue Zenghui,Lei Bingxu,Fang Shu.Core Patents Mining based on Cross Co-occurrence Analysis to Patent Technology-Effect Subject Terms and Citations[J].Library and Information Service,2014,58(4):59-64. |
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| Authors: | Xu Haiyun Yue Zenghui Lei Bingxu Fang Shu |
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| Institution: | 1. Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041;2. University of Chinese Academy of Sciences, Beijing 100190 |
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| Abstract: | Based on analysis to the current core patent mining methods, we constructed technology-effect subject terms and citations cross co-occurrence to mine core patent clusters. Through the cross co-occurrence analysis to patent technology-effect subject terms, we can easily identify the technology-effect of core patent clusters. Meanwhile, the technology-effect subject terms also get a cross co-occurrence through patent citations, which help to identify the relationship of the various technology in a special field. In this article, we chose medical microendoscopic as empirical study to display the characteristics of cross co-occurrence way, advantages and disadvantages among the cross co-occurrence of technology-effect subject terms with patent citations, co-occurrence of technology subject term with patent citations and the patent co-citation co-occurrence analysis in visualization network map. |
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| Keywords: | topic mining citation analysis co-occurrence analysis patent technology-effect matrix |
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