基于作者研究相似性的CLC与IPC类目同现映射研究

Co-occurrence Mapping between Chinese Library Classification and International Patent Classification Categories Based on Author's Research Similarity

期刊论文与专利文献间的知识流动展现了科学研究与技术创新的演进路线。对中国图书馆分类法(Chinese Library Classification,CLC)和国际专利分类法(International Patent Classification,IPC)进行类目映射分析,有助于打通论文资源与专利资源之间的壁垒,识别不同学科领域内论文与专利之间的科技发展特性。本文提出了一种将社会网络分析思想与同现映射相融合的映射方法:将同一作者且研究主题高度相关的论文与专利进行一对一组合作为单位数据,利用CLC与IPC同时对每个单位数据进行分类标注,结合类目相似度计算,对数据集的标注结果进行分析。最终,得到具有普适性的CLC与IPC类目间的一对一映射、一对多映射以及双向映射关系。

Knowledge flow between papers and patents reflects the evolutionary route of scientific research and technological innovation.Category mapping analysis of Chinese Library Classification(CLC)and International Patent Classification(IPC)is helpful in breaking through the barriers between paper and patent resources,by identifying the characteristics of scientific and technological development between papers and patents of different disciplines.This study proposes a mapping method that integrates the idea of social network analysis with co-occurrence mapping.Taking the papers and patents of the same author and highly related research topics as unit data,CLC and IPC are used to classify and label each unit of data simultaneously,thereby combining category similarity calculations and analyzing the labeling results of the dataset.Finally,universal one-to-one,one-to-many,and two-way mappings between the CLC and IPC categories are obtained.