基于专利异构网络的中小企业潜在合作伙伴研究——以石墨烯领域为例
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摘要
中小企业的技术创新能力已经成为一个国家创新体系中不可缺少的重要部分。美国对中小企业的相关扶持措施非常全面,鼓励中小企业通过从非营利性科研机构以及大型企业那里获取外部合作来帮助自身发展。本研究通过测度专利之间的相似性来度量专利权人之间的技术相似性,利用美国专利授权库中石墨烯专利数据构建专利异构网络,根据PathSelClus算法的需要,从专利权人中选出了7家科研机构与5家大型企业作为用户指导的聚类依据,根据专利单一属性信息与综合利用专利的多种属性信息聚类依据得到两种不同语义的聚类结果,在同一聚簇中的中小企业被认为与该科研机构(大型企业)存在技术相似性,具有合作的潜力。最后选出20家该领域中小企业代表,通过对中小企业与科研院所已有合作的调查以及中小企业与大型企业的主要关注领域进行对比,发现部分中小企业与科研机构之间存在过合作关系,同时中小企业与大型企业也有相同的主要关注领域,表明基于专利信息构建异构网络后利用PathSelClus算法对专利权人进行聚类的方法,在一定程度上对评估潜在合作伙伴是有效的。
The technological innovation capability of small-and medium-sized enterprises(SMEs) has become an important component of a country's national innovation system. In the US, there have been very comprehensive support measures related to SMEs that encourage them to cooperate with external organizations, such as non-profit research institutions and large enterprises; the support, in turn, can help them to develop rapidly. This study seeks to measure the technical similarity of the patentees by measuring the similarity between their patents and builds a heterogeneous network based on graphene-related patents in the database on US-authorized patents. Further, based on the PathSelClus algorithm, we choose 7 scientific research institutions and 5 large enterprises to act as user guidance. As a result, we get 2 types of clusters in terms of 2 semantics. The first result is a distribution of the degree of similarity between the SMEs and the 7 scientific research institutions, and the other is a distribution of the degree of similarity between SMEs and the 5 large enterprises. Finally,this study analyzes the validity of clustering results according to the research and development direction of the patentees.
The technological innovation capability of small-and medium-sized enterprises(SMEs) has become an important component of a country's national innovation system. In the US, there have been very comprehensive support measures related to SMEs that encourage them to cooperate with external organizations, such as non-profit research institutions and large enterprises; the support, in turn, can help them to develop rapidly. This study seeks to measure the technical similarity of the patentees by measuring the similarity between their patents and builds a heterogeneous network based on graphene-related patents in the database on US-authorized patents. Further, based on the PathSelClus algorithm, we choose 7 scientific research institutions and 5 large enterprises to act as user guidance. As a result, we get 2 types of clusters in terms of 2 semantics. The first result is a distribution of the degree of similarity between the SMEs and the 7 scientific research institutions, and the other is a distribution of the degree of similarity between SMEs and the 5 large enterprises. Finally,this study analyzes the validity of clustering results according to the research and development direction of the patentees.
引文
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[23]温芳芳.基于专利权人-分类号多重共现分析的全球专利布局研究——以太阳能汽车技术领域为例[J].现代情报, 2017(4):165-169.
[24]李立.基于元路径选择和融合的异构信息网络社区挖掘算法研究[D].西安:西安电子科技大学, 2014:8-17.
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[27] Sun Y Z, Han J W, Yan X F, et al. Pathsim:Meta path-based top-k similarity search in heterogeneous information networks[J]. Proceedings of the VLDB Endowment, 2011, 4(11):992-1003.
[28]葛军.异构信息网络上的相似性搜索研究[D].西安:西安电子科技大学, 2013:5-9.
[29]步少莉.异构信息网络Top-k相关性查询方法研究[D].山东:山东大学, 2014:5-8.
[30]梅进,张笑. 2010年诺贝尔物理学奖揭晓[EB/OL].(2010-10-5)[2016-06-20]. http://news. sciencenet. cn/htmlnews/2010/10/238322.shtm.
[31]李明珍.石墨烯领域发展态势及对策建议[J].科技与经济,2014, 27(1):101-105.
[32]叶春涛.国外中小企业技术创新的现状及启示[J].经济研究导刊, 2013(22):259-260.
[2]张小冲,张学军.经济体制改革前沿问题:国际比较与借鉴[M].北京:人民出版社, 2003.
[3]赵亚静.我国中小企业知识产权建设政策体系研究[D].长春:东北师范大学, 2012:5-7.
[4]杨西春.中小企业技术创新与政策环境优化[J].人民论坛,2014(1):87-89.
[5]李英.提升浙江省中小企业国际竞争力的对策研究[D].杭州:浙江工业大学, 2008:6-10.
[6]李敏.试析自主创新对他国经济发展的影响与作用[N/OL].中国信息报,(2007-08-22)[2016-6-23]. http://www. zgxxb. com.cn/jsdk/201002244255. shtm.
[7]洪勇,李英敏.基于专利耦合的企业间技术相似性可视化研究[J].科学学研究, 2013, 31(7):1013-1021.
[8]徐元浩.基于潜在语义分析的专利文献分析与搜索技术的研究[D].杭州:浙江大学, 2010:7-8.
[9] Kessler M M. Bibliographic coupling between scientific papers[J]. American Documentation, 1963, 35(3):401-420.
[10] Huang M H, Chiang M H, Li Y, et al. Constructing a patent citation map using bibliographic coupling:A study of Taiwan’s hightech companies[J]. Scientometrics, 2003, 58(3):489-505.
[11]吕惠琳,刘杨.基于专利耦合的波导光互联企业技术相似性分析[J].情报探索, 2016(1):8-11.
[12] Small H. Cocitation in the scientific literature:a new measure of the relationship between two documents[J]. Journal of American Society for Information Science, 1973, 24(4):265-269.
[13] White H D, Griffield B C. Author cocitation:A literature measure of intellectual structure[J]. Journal of the American Society for Information Science, 1981, 32(3):163-171.
[14] Magerman T, Van Looy B, Song X Y. Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications[J]. Scientometrics, 2010, 82(2):289-306.
[15]陈芨熙,顾新建,陈国海.基于向量空间模型和专利文献特征的相似专利确定方法[J].浙江大学学报(工学版), 2009, 43(10):1848-1852.
[16] Yoon B, Park Y. A text-mining-based patent network:Analytical tool for high-technology trend[J]. The Journal of High Technology Management Research, 2004, 15(1):37-50.
[17]季铎,毕臣,蔡东风.基于类别信息优化的潜在语义分析分类技术[J].中国科学技术大学学报, 2015, 45(4):314-320.
[18] Janssens F. Clustering of science fields by integrating text mining and bibliometrics[D]. Belgium:Katholieke University Leuven,2007.
[19] Liu X H, Yu S, Frizo J, et al. Weighted hybrid clustering by combing text mining and bibliometrics on a large-scale journal database[J]. Journal of the American Society for Information Science and Technology, 2010, 61(6):1105-1119.
[20]任红娟.基于文献内容和引用结合的知识结构分析方法研究[D].北京:中国科学院研究生院, 2010.
[21] Wang F, Ding C, Li T. Integrated KL(K-means-Laplacian)clustering:A new clustering approach by combining attribute data and pairwise relations[C]//Proceedings of the 2009 SIAM International Conference on Data Mining. DBLP, 2009:38-48.
[22]温芳芳.专利分类号耦合分析在企业潜在合作关系识别中的应用[J].现代情报, 2018(7):142-147.
[23]温芳芳.基于专利权人-分类号多重共现分析的全球专利布局研究——以太阳能汽车技术领域为例[J].现代情报, 2017(4):165-169.
[24]李立.基于元路径选择和融合的异构信息网络社区挖掘算法研究[D].西安:西安电子科技大学, 2014:8-17.
[25] Sun Y, Han J. Meta-path-based search and mining in heterogeneous information networks[J]. Tsinghua Science and Technology, 2013, 18(4):329-338.
[26] Sun Y Z, Norick B, Han J W, et al. PathSelClus:Integrating metapath selection with user-guided object clustering in heterogeneous information networks[J]. ACM Transactions on Knowledge Discovery from Data, 2013, 7(3):Article No. 11.
[27] Sun Y Z, Han J W, Yan X F, et al. Pathsim:Meta path-based top-k similarity search in heterogeneous information networks[J]. Proceedings of the VLDB Endowment, 2011, 4(11):992-1003.
[28]葛军.异构信息网络上的相似性搜索研究[D].西安:西安电子科技大学, 2013:5-9.
[29]步少莉.异构信息网络Top-k相关性查询方法研究[D].山东:山东大学, 2014:5-8.
[30]梅进,张笑. 2010年诺贝尔物理学奖揭晓[EB/OL].(2010-10-5)[2016-06-20]. http://news. sciencenet. cn/htmlnews/2010/10/238322.shtm.
[31]李明珍.石墨烯领域发展态势及对策建议[J].科技与经济,2014, 27(1):101-105.
[32]叶春涛.国外中小企业技术创新的现状及启示[J].经济研究导刊, 2013(22):259-260.