一种融合多维关系的地理环境时空主题发现方法  

A Method for Geographical Environment Spatiotemporal Topic Discovery of Multi-dimensional Relationships

作　　者：朱杰 张宏军 廖湘琳 徐有为 ZHU Jie;ZHANG Hongjun;LIAO Xianglin;XU Youwei(College of Command and Control Engineering,Army Engineering University,Nanjing 210002,China;Troops 73021,Hangzhou 315023,China)

机构地区：[1]陆军工程大学指挥控制工程学院,江苏南京210002 [2]73021部队,浙江杭州315023

出　　处：《武汉大学学报（信息科学版）》2024年第2期291-302,共12页Geomatics and Information Science of Wuhan University

基　　金：中国博士后科学基金(2019M664028)。

摘　　要：对战场文本数据的深入挖掘,可以高质量和高效率地发现时空主题结构,从而有效揭示战场事件发展的时空规律。针对现有的主题发现方法无法有效适用于具有多维异构关系的时空主题发现,提出了一种融合多维关系联合聚类的时空主题发现方法,首先构建以地理环境实体、地理位置与事件主题为节点的主题关系网络;然后以张量模型的Tucker分解建立主题关系的完全表达式作为主题分类的目标函数;最后运用块值矩阵分解方法进行联合聚类计算,获取主题分类结果和内聚结构。实验结果表明,该方法能够有效发现具有时空语义关系特征的主题结构,较好地体现出地理环境要素与时空主题之间的关联性,以及时空主题在地理位置与事件主题标签上的内聚性,反映出主题的演化过程。Objectives:Using battlefield text data for spatiotemporal topic analysis,we can obtain the spatial distribution pattern of geographical environment elements and their impact characteristics on battlefield activities from micro to macro and from scattered to gathering places,and mine the spatial distribution and development law of battlefield events,which further enriches the perception mode of battlefield environment and provides a new means of battlefield environment efficiency analysis.It is of great significance and value for in-depth understanding of battlefield environmental knowledge.Methods:The key technology to improve the quality of spatiotemporal topic discovery in geographical environment is to effectively construct entity composite relationship network and integrate multi-dimensional heterogeneous relationships for topic clustering.First,a spatiotemporal topic tensor model integrating multi-dimensional relationships is constructed,and the complete expression of topic relationship is given by using the Tucker decomposition of topic tensor model.Then,the feature vector space of multi-dimensional relational clustering is constructed as the objective function of topic classification,and the block value matrix decomposition technology is used for joint clustering calculation,and the core tensor matrix is used to solve the problem of data sparsity.Finally,the block value matrix obtained by multi-dimensional relational clustering is used to obtain the association value between geographical environment elements and spatiotemporal topics.Results:The results show that:(1)The geographical environment entities and entity relationships are correctly clustered into spatiotemporal topic structure.The accuracy rates in the training set were 88.4%and 86.9%respectively,and in the test set were 87.3%and 85.8%respectively.(2)The number of entities and tags clustered under different subject structures decreases gently with the reduction of subject scale.The statistical results show that the most subject tags are maneuver,

关 键 词：地理环境 多维关系 时空主题发现 块值矩阵分解 联合聚类 

分 类 号：P208[天文地球—地图制图学与地理信息工程]