摘要
【目的】为促进跨地区的专利合作与知识交流,充分挖掘专利的人物关系特征和内容特征,扩展创新合作空间,优化创新要素的分配,整体提升创新水平,提出一种融合LDA与决策树模型的跨地区专利合作关系发现方法。【方法】选取incoPat专利数据库中广东省和武汉市985高校的22 855条专利数据。利用LDA主题模型,对专利的领域离散度、权威度和技术度三个维度进行主题抽取和聚类,构建决策树并调整决策边界,从而动态识别出最优合作关系;最后根据发明人网络有效规模值选出最优挖掘策略,从而实现合作关系的发现与推荐。【结果】该方法可从专利数量排名前4的专利大类里发现18对潜在跨地区合作关系,而在链路预测方法下,合作网络的节点邻接关系稀疏,无法完成跨地区合作关系推荐。【局限】采集的数据范围有限,且需进一步从横向和纵向两个方面考虑企业等产学研主体和技术产业链上、中、下游对实际创新生态的影响,确认方法的适用性。【结论】融合LDA与决策树的跨地区专利合作关系发现方法,可以有效识别网络中的潜在合作关系,充分发挥不同地区之间的领域组合在提升创新水平上的优势,为跨地区、多领域条件下的专利合作提供参考。
[Objective]This paper proposes an algorithm to identify potential collaboration opportunities for patents with the LDA and decision tree models,aiming to enhance the cross-region innovation.[Methods]First,we retrieved 22855 patents from the inco Pat database,which were developed by higher education institutions from Guangdong Province and Wuhan City.Then,we used the LDA to extract and cluster patent topics.Third,we constructed decision tree to identify the best potential cooperative relations by adjusting the decision boundaries.Finally,we chose the optimal data mining strategy based on the effective size of the inventors’network,which helps us identify and recommend cooperative relationships.[Results]We found 18 pairs of potential crossregional partners from the top four patent categories in the data set,which was much better than the link prediction method.[Limitations]The coverage of patent data needs to be expanded.More research is also needed to study the impacts of the university and industry on the innovation ecology.[Conclusions]The proposed method could identify the potential cross region partners for patents and innovation.
作者
陈浩
张梦毅
程秀峰
Chen Hao;Zhang Mengyi;Cheng Xiufeng(School of Information Management,Central China Normal University,Wuhan 430079,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第10期37-50,共14页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金面上项目(项目编号:71974069)的研究成果之一。
关键词
专利合作
决策树
主题模型
跨地区
Patent Cooperation
Decision Tree
Topic Model
Cross-Region
