基于时序图的作战指挥行为知识表示学习方法

doi:10.3969/j.issn.1001-506X.2020.11.14

Abstract

Abstract:

In order to explore the modeling method of the sequential operations command behaviors knowledge deeply and effectively obtain the temporal correlation features of operations command behaviors of the commanders, an operations command behaviors knowledge representation learning method is proposed based on the sequential graph of wargaming operational orders. The operations command behaviors are presented by knowledge representation learning, and the model is verified by the task of operations command behaviors prediction. Experimental results show that the evaluating index is increased obviously, and the spatial-temporal features of operations command behaviors by the joint operations commanders are obtained effectively. Our work highlights the sequential joint operations command behavior representation learning and provides a foundation for the command experience extraction of joint operations commanders and joint operations situation cognition.

Key words: operations command behavior, sequential graph, knowledge representation learning, graph embedding, joint operation

CLC Number:

TP391.9

Baokui WANG, Lin WU, Xiaofeng HU, Xiaoyuan HE, Shengming GUO. Operations command behavior knowledge representation learning method based on sequential graph[J]. Systems Engineering and Electronics, 2020, 42(11): 2520-2528.

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References 24

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数据集	实体数	关系数	实体属性类型	稀疏度
亚马逊电子产品数据集	300 150	3 740 196	3	0.996
作战指挥行为数据集	26 500	2 106 671	3	0.997

模型	亚马逊电子产品数据集	作战指挥行为数据集
BGE	0.932 7	0.847 3
LINE(1st)	0.955 4	0.850 7
LINE(2nd)	0.866 4	0.874 2
metapath2vec	0.964 5	0.880 1
时序作战指挥行为知识表示学习模型	0.972 5	0.921 2

模型	亚马逊电子产品数据集	作战指挥行为数据集
BGE	0.154 6	0.192 1
LINE(1st)	0.132 0	0.157 3
LINE(2nd)	0.143 2	0.166 5
metapath2vec	0.120 1	0.124 9
时序作战指挥行为知识表示学习模型	0.087 5	0.085 6

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Operations command behavior knowledge representation learning method based on sequential graph

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