Exploring the topic hierarchy of a research field can help us better recognize its intellectual structure. This paper proposes a new method to automatically discover the topic hierarchy, in which the keyword network is constructed to represent topics and their relations, and then decomposed hierarchically into shells using the K-core decomposition method. Adjacent shells with similar morphology are merged into layers according to their density and clustering coefficient. In the keyword network of the digital library field in China, we discover four different layers. The basic layer contains 17 tightly-interconnected core concepts which form the knowledge base of the field. The middle layer contains 13 mediator concepts which are directly connected to technology concepts in the basic layer, showing the knowledge evolution of the field. The detail layer contains 65 concrete concepts which can be grouped into 13 clusters, indicating the research specializations of the field. The marginal layer contains peripheral or isolated concepts.
['Lu Xiao', 'Guo Chen', 'Jianjun Sun', 'Shuguang Han', 'Chengzhi Zhang']
Exploring the topic hierarchy of digital library research in China using keyword networks: a K-core decomposition approach