Latent semantic understanding of geographical environment spatio-temporal data based on topic model

doi:10.11947/j.AGCS.2021.20200380

Abstract

Abstract: Text is an important data mode of battlefield information. Mining spatial-temporal semantic information of geographical environment from battlefield text is an important method for machine to understand battlefield environment, which is helpful to expand battlefield environment spatial cognition and understanding. A method based on topic model is designed to reflect the semantic relationship between geographical spatio-temporal factors and event topics, and different topic classification with its distribution of word features are formed by the method of information extraction to mine the relevant information of topic elements; the joint distribution model of event topic and geographical spatio-temporal semantic features is established to automatically discover the correlation among time, space and event topics, thus generating the latent geographical spatio-temporal semantic topics; through the experimental verification and the application practice, we believe that the law of spatio-temporal distribution under different topics can be seek by using correlation between the event topics and location information with spatial analysis method, so as to provide the basis for the location prediction of new events and the countermeasures of seeking advantages and avoiding disadvantages, and expand the traditional thematic analysis of geographical events.

Key words: topic model, geographical environment, spatio-temporal data, semantic understanding, spatial analysis

CLC Number:

P208

ZHU Jie, ZHANG Hongjun, LIAO Xianglin, TIAN Jiangpeng. Latent semantic understanding of geographical environment spatio-temporal data based on topic model[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1404-1415.

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