Linking geo-models for geomorphological classification using knowledge graphs

Abstract

Geographic computation is an important process in geographic information systems to detect, predict, and simulate geographic entities, events, and phenomena, which is performed through a series of geographic models over geographic data. However, selecting and sequencing appropriate models is challenging for users with limited knowledge. To automate the process of linking models into workflows, a knowledge graph-based approach is proposed. In this approach, the first part is to construct a knowledge graph that integrates knowledge from geographic models and domain experts. Then, an algorithm is designed to assist the constructed knowledge graph in automating model linking. This paper takes the geomorphological classification of the Hengduan Mountains in China as a case study, which geomorphological classification maps are generated by performing querying and computing through the geomorphological classification knowledge graph. Experimental results demonstrate that the proposed knowledge graph-based approach links the models into workflows automatically and generates reliable classification results.