A hierarchical spatio-temporal object knowledge graph model for dynamic scene representation |
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| Authors: | Xinke Zhao Yibing Cao Jiahe Wang Xinhua Fan Minjie Chen |
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| Affiliation: | 1. Institute of Geospatial Information, Information Engineering University, Zhengzhou, China;2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China |
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| Abstract: | Spatio-temporal knowledge is essential in understanding the dynamic aspects of complex scenes. However, existing knowledge graphs have limitations, such as inadequate time description, inflexible expression of semantic relationships, and difficulties in accessing GIS platforms. The article proposes the spatio-temporal object knowledge graph (STOKG), consisting of the object concept layer, spatio-temporal object layer, and dynamic version layer. To demonstrate the practical usefulness of the STOKG model, the Henan epidemic knowledge graph is created using epidemiological data from early 2020, which shows the dynamic evolution of the spatio-temporal objects of cases from the geography and semantic perspectives. Finally, the STOKG model is compared with the existing models in terms of accuracy, completeness and repetitiveness. The experimental results show that the STOKG model provides a more flexible and comprehensive approach to representing spatio-temporal knowledge, which is useful for applications in fields such as geography, epidemiology, and environmental science. |
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