A hierarchical spatio‐temporal object knowledge graph model for dynamic scene representation

Author:

Zhao Xinke1,Cao Yibing1,Wang Jiahe23,Fan Xinhua1,Chen Minjie1

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

3. University of Chinese Academy of Sciences Beijing China

Abstract

AbstractSpatio‐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.

Funder

National Key Research and Development Program of China

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Spatio-temporal knowledge embedding method considering the lifecycle of geographical entities;International Journal of Applied Earth Observation and Geoinformation;2024-07

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