Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual infor-mation among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsuper-vised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighbor-hood mutual information is proposed, and the feature clusters with certain event correlation are ob-tained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l1 norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding al-gorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.

Jing-Tao Sun;Jing-Ming Li;Qiu-Yu Zhang

School of Computer Science and Technology,Xi'an University of Posts and Telecommunications,Xi'an,Shaanxi,710121,China;Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,Xi'an University of Posts and Telecommunications,Xi'an,Shaanxi,710121,China;School of Management Science and Engineering,Anhui University of Finance and Economics,Bengbu,Anhui,230030,China;School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu,730050,China

防务技术

[1]Jing-Tao Sun;Jing-Ming Li;Qiu-Yu Zhang-.Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation)[J].防务技术,2022(12):2150-2159

UKGE,unmarked,unsuper

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