基于深度学习的期刊分群与科学知识结构测度方法研究

The Measurement of Scientific Knowledge Structure Based on Journal Clustering and Deep Learning

准确地研究和测度科学知识之间的逻辑关系和结构体系,是进行科学政策研究和科研项目资助布局等科研管理活动的重要基础。学术期刊作为科学知识传播和交流的重要平台,是探测科学知识结构的一种有效载体,但是不同的学术期刊分类体系对科学知识结构的测度结构会产生直接而广泛的影响。文章从学术期刊分群的角度出发,考虑期刊在共被引过程中的距离因素,通过采用深度学习算法,来进行期刊的相似度计算与分群问题研究,在此基础上进行科学知识结构测度方法研究,并以中国人文社会科学期刊引文数据库为实验对象进行了实证研究。从实证结果来看,我国人文社会科学学科知识结构存在较为明显的结构划分,不同学科类别或不同研究领域的期刊都被分到了相应的群组,表明从期刊使用的角度来看,我国人文社会科学知识结构边界是相对较为清晰的。在此基础上重点对法学期刊的两个群组的科学研究主题进行了挖掘,从关键词的共现网络中可以明显看出,两个期刊群体内的研究主题虽有一定的交叉,但是两者在具体研究内容上也存在着显著区别。

Accurately studying and measuring the logical relationship and structural system of scientific knowledge is an important basis for scientific research management activities. As an important platform for the dissemination and exchange of scientific knowledge, academic journals are effective carriers for exploring the structure of scientific knowledge. By using a deep learning algorithm, this paper considers the distance factor in the process of co-citation, studies similarity in journal clustering, and constructs a method for scientific knowledge structure measurement. The experimental results show that the knowledge structure in the humanities and social sciences in China is divided into distinct structures, and journals of different disciplines or research fields are divided into corresponding groups. This indicates that the knowledge structure boundary of the humanities and social sciences in China is relatively clear from the perspective of journal use. This study primarily explored the scientific research topics of the two groups of law journals. From the co-occurrence network of keywords, it can be clearly seen that although the research topics of the two groups of law journals overlap to a certain extent,there are significant differences in the specific research contents between the two groups.