A knowledge representation model based on the geographic spatiotemporal process. Issue 4 (3rd April 2022)

• Record Type: Journal Article
• Authors: Zheng, Kun; Xie, Ming Hui; Zhang, Jin Biao; Xie, Juan; Xia, Shu Hao
• Is Part Of: International journal of geographical information science. Volume 36:Issue 4(2022)
• Page Start: 674
• Page End: 691
• Publication Date: 2022-04-03
• Subjects: Knowledge graph -- geographic spatiotemporal process -- time -- geo-events -- geo-entities' evolution; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2021.1962527
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons.