[Purpose/significance] This paper proposes a new hierarchical discovery method of scientific knowledge structure, which provides reference for optimizing knowledge structure discovery process and improving knowledge organization form.[Method/process] Firstly, this paper constructed a hierarchical discovery method of scientific knowledge structure by using LDA topic model. Then, according to the average similarity degree among topics, it automatically determined the hierarchy of knowledge structure, and the literature subsets were intersected by filtering threshold automatically in the "document-topic" probability matrix. Finally, it adopted tree diagram to display the science knowledge structure and explore the correlation and inheritance of knowledge points. Besides, we also compared our method with HLDA method which is a hierarchical topic model.[Result/conclusion] The result shows that the knowledge structure obtained by our method is better, the representation of knowledge topic is stronger and it has the higher operation efficiency. In addition, compared with the HLDA method, our method has a great improvement on the topic differences of the single layer and the topic inheritance between layers.
Li Hui
,
Tian Yadan
. A Hierarchical Discovery Method of Scientific Knowledge Structure[J]. Library and Information Service, 2018
, 62(13)
: 92
-102
.
DOI: 10.13266/j.issn.0252-3116.2018.13.012
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