将学科交叉的定量化研究分为计量指标和可视化两种方式,深入分析Rao-Stirling、信息熵、中介中心度、网络密度和网络核心度5种指标的计量差异和学科交叉覆盖地图的可视化方式。在此基础上,以Web of Science数据库收录的2001-2010年间情报学期刊论文为数据源做学科交叉度计量的实证研究,分析5种交叉度计量指标的计量特征和指标间的相关关系。研究发现:情报学在这10年间并未与与本学科跨度较大的学科形成更多交叉,同时情报学在其研究领域内的核心地位有所减弱,并通过学科交叉覆盖图展示情报学研究领域的范围以及与情报学关系最为密切的学科领域。
Purpose: This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach: The analysis framework includes the following steps: 1) co-classification analysis based on the International Patent Classification(IPC) codes and Derwent Manual Codes(DMC) to detect sub-tech fields, 2) keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3) social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute.Findings: The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents.Research limitations: In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications: The analysis framework provides valuable information for decisionmakers of large institutions which have many patents with broad application prospects.Originality/value: Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.
