产业链视角下基于图嵌入的专利布局意图挖掘方法研究

doi:10.3772/j.issn.1000-0135.2022.05.001

情报学报

2022, Vol. 41

Issue (5): 437-450 DOI: 10.3772/j.issn.1000-0135.2022.05.001

情报理论与方法

本期目录 | 过刊浏览 | 高级检索

产业链视角下基于图嵌入的专利布局意图挖掘方法研究

翟东升, 阚慧敏, 李梦洋, 徐硕, 陈蒙蒙

北京工业大学经济与管理学院，北京 100124

Research on Mining Patent Layout Intention Based on Graph Embedding from the Perspective of Industrial Chain

Zhai Dongsheng, Kan Huimin, Li Mengyang, Xu Shuo, Chen Mengmeng

School of Economics and Management, Beijing University of Technology, Beijing 100124

摘要
图/表
参考文献
相关文章 (0)

全文: PDF (4166 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要知识经济背景下，国家间的技术竞争日益激烈，而中国在专利布局方面存在认知与操作短板，制约着中国高新技术产业的发展。因此，提出一套有效的专利布局分析方法，对指导企业进一步实施专利布局具有重要意义。当前，专利布局的分析视角相对局限于企业层面，且传统的布局分析方法不能直观展现企业间的技术攻防意图，所以，本文在刻画产业链专利分布结构的基础上，总结基本的专利分布模式及其蕴含的布局意图，以此实现产业链视角下的专利布局意图挖掘。首先，在微观层面引入结构性与功能性技术关联关系，修正专利知识组织结构，并根据修正后的专利知识图谱本体构建领域专利知识图谱；然后，利用图嵌入算法刻画产业链专利分布结构；接着，总结5种基本的专利分布模式并分析其布局意图，基于此挖掘产业链各布局主体的布局意图；最后，通过在非全氟化质子交换膜领域进行实证研究，验证了上述方法的有效性与实用性，并为我国非全氟化质子交换膜企业以及产业的安全发展提出了应对建议。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	翟东升
	阚慧敏
	李梦洋
	徐硕
	陈蒙蒙

关键词 ：专利布局, 意图挖掘, 专利知识图谱, 图嵌入

收稿日期: 2021-05-06

基金资助:国家自然科学基金面上项目“科技关联视角下新兴技术弱信号扫描预判方法研究”（72074014）。

作者简介: 翟东升，男，1963年生，博士，教授，博士生导师，主要研究领域为信息管理与信息系统、专利挖掘与分析；阚慧敏，女，1996年生，硕士，主要研究领域为专利数据分析；李梦洋，男，1988年生，博士，主要研究领域为专利分析与专利投资，E-mail：lmy8869@126.com；徐硕，男，1979年生，博士，教授，博士生导师，主要研究领域为信息分析、科学计量学、科技情报分析和数据挖掘；陈蒙蒙，女，1995年生，硕士，主要研究领域为信息管理与信息系；

引用本文:

翟东升, 阚慧敏, 李梦洋, 徐硕, 陈蒙蒙. 产业链视角下基于图嵌入的专利布局意图挖掘方法研究[J]. 情报学报, 2022, 41(5): 437-450.
Zhai Dongsheng, Kan Huimin, Li Mengyang, Xu Shuo, Chen Mengmeng. Research on Mining Patent Layout Intention Based on Graph Embedding from the Perspective of Industrial Chain. 情报学报, 2022, 41(5): 437-450.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.05.001 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I5/437

1 张跃东, 卫平, 胡冰. 中国企业在非对称国际竞争中的专利战略实施状况——基于七省市企业调查问卷[J]. 中国科技论坛, 2019(2): 118-125.
2 Ernst H. Patent portfolios for strategic R&D planning[J]. Journal of Engineering and Technology Management, 1998, 15(4): 279-308.
3 Blind K, Cremers K, Mueller E. The influence of strategic patenting on companies’ patent portfolios[J]. Research Policy, 2009, 38(2): 428-436.
4 Yang Q, Minutolo M C. The strategic approaches for a new typology of firm patent portfolios[J]. International Journal of Innovation and Technology Management, 2016, 13(2): 1650012.
5 赵蓉英, 李新来, 李丹阳. 专利引证视角下的核心专利研究——以人工智能领域为例[J]. 情报理论与实践, 2019, 42(3): 78-84.
6 Lee B K, Sohn S Y. Patent portfolio-based indicators to evaluate the commercial benefits of national plant genetic resources[J]. Ecological Indicators, 2016, 70: 43-52.
7 Li H, Liu L G, Huo J T, et al. Research on patent portfolio design by using of TRIZ method[J]. MATEC Web of Conferences, 2016, 65: 01010.
8 贡小妹, 黄帅, 敦帅, 等. 专利视角下科技型企业竞争力提升路径探究——以华为公司发展的动态过程为例[J]. 科技管理研究, 2018, 38(4): 155-160.
9 刘贵富, 赵英才. 产业链: 内涵、特性及其表现形式[J]. 财经理论与实践, 2006, 27(3): 114-117.
10 贾丽臻, 张换高, 张鹏, 等. 基于专利地图的企业专利布局设计研究[J]. 工程设计学报, 2013, 20(3): 173-179.
11 Wu Y, Fang J. Prediction of technology trend of educational robot industry based on patent map analysis[C]// Proceedings of the International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Cham: Springer, 2021: 149-155.
12 Yang X, Liu X, Song J. A study on technology competition of graphene biomedical technology based on patent analysis[J]. Applied Sciences, 2019, 9(13): 2613.
13 Yu X, Zhang B. Obtaining advantages from technology revolution: a patent roadmap for competition analysis and strategy planning[J]. Technological Forecasting and Social Change, 2019, 145: 273-283.
14 Grimaldi M, Cricelli L, Di Giovanni M, et al. The patent portfolio value analysis: a new framework to leverage patent information for strategic technology planning[J]. Technological Forecasting and Social Change, 2015, 94: 286-302.
15 Yoon B, Magee C L. Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction[J]. Technological Forecasting and Social Change, 2018, 132: 105-117.
16 卞秀坤, 郑素丽, 诸葛凯, 等. 基于ISM模型的企业专利组合核心特征分析[J]. 科技管理研究, 2020, 40(3): 95-100.
17 Lin B W, Chen C J, Wu H L. Patent portfolio diversity, technology strategy, and firm value[J]. IEEE Transactions on Engineering Management, 2006, 53(1): 17-26.
18 Luo J X, Yan B W, Wood K. InnoGPS for data-driven exploration of design opportunities and directions: the case of google driverless car project[J]. Journal of Mechanical Design, 2017, 139(11): 111416.
19 Appio F P, De Luca L M, Morgan R, et al. Patent portfolio diversity and firm profitability: a question of specialization or diversification?[J]. Journal of Business Research, 2019, 101: 255-267.
20 Wang Y L, Richard R, McDonald D. Competitive analysis with graph embedding on patent networks[C]// Proceedings of the 2020 IEEE 22nd Conference on Business Informatics. IEEE, 2020: 10-19.
21 王亦凡, 李继云. 基于异构图嵌入学习的相似病案推荐[J]. 计算机系统应用, 2020, 29(10): 228-234.
22 Moon C, Jin C M, Dong X L, et al. Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses[J]. Journal of Biomedical Informatics, 2021, 119: 103838.
23 Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2014: 701-710.
24 Bordes A, Weston J, Collobert R, et al. Learning structured embeddings of knowledge bases[C]// Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2011: 301-306.
25 Socher R, Chen D Q, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2013: 926-934.
26 Nickel M, Tresp V, Kriegel H P. A three-way model for collective learning on multi-relational data[C]// Proceedings of the 28th International Conference on International Conference on Machine Learning. Madison: Omnipress, 2011: 809-816.
27 Nickel M, Tresp V, Kriegel H P. Factorizing YAGO: scalable machine learning for linked data[C]// Proceedings of the 21st International Conference on World Wide Web. New York: ACM Press, 2012: 271-280.
28 Bordes A, Usunier N, Garcia-Durán A, et al. Translating embeddings for modeling multi-relational data[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2013: 2787-2795.
29 Wang Z, Zhang J W, Feng J L, et al. Knowledge graph embedding by translating on hyperplanes[C]// Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2014: 1112-1119.
30 Lin Y K, Liu Z Y, Sun M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2181-2187.
31 Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: a survey[J]. Knowledge-Based Systems, 2018, 151: 78-94.
32 马天旗. 专利布局[M]. 北京: 知识产权出版社, 2016.
33 李清海, 刘洋, 吴泗宗, 等. 专利价值评价指标概述及层次分析[J]. 科学学研究, 2007, 25(2): 281-286.
34 李春燕, 石荣. 专利质量指标评价探索[J]. 现代情报, 2008, 28(2): 146-149.
35 刘杭. 层次分析中的两种近似计算方法[J]. 南京邮电学院学报, 1987, 7(4): 135-139.
36 Saaty T L. Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process[J]. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas, 2008, 102(2): 251-318.