LAUSR

Global problems all occur at a particular location on or near the Earth’s surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much attention worldwide. Although the existing KGs are constructed from internet encyclopedias and contain abundant knowledge, they lack exact coordinates and geographical relationships. In light of this, a geographical knowledge graph (GeoKG) construction method based on multisource data is proposed, consisting of a modeling schema layer and a filling data layer. This method has two advantages: (1) the knowledge can be extracted from geographic datasets; (2) the knowledge on multisource data can be represented and integrated. Firstly, the schema layer is designed to represent geographical knowledge. Then, the methods of extraction and integration from multisource data are designed to fill the data layer, and a storage method is developed to associate semantics with geospatial knowledge. Finally, the GeoKG is verified through linkage rate, semantic relationship rate, and application cases. The experiments indicate that the method could automatically extract and integrate knowledge from multisource data. Additionally, our GeoKG has a higher success rate of linking web pages with geographic datasets, and its exact coordinates have increased to 100%. This paper could bridge the distance between a Geographic Information System and a KG, thus facilitating more geospatial applications.