The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a lack of hierarchical modeling and insufficient coverage of geographic knowledge (e.g., limited entity types, inadequate attributes, and insufficient spatial relationships), which hinders their effective use and representation of semantic content. This paper presents HGeoKG, a hierarchical geographic knowledge graph that comprehensively models hierarchical structures, attributes, and spatial relationships of multi-type geographic entities. Based on the concept and construction methods of HGeoKG, this paper developed a dataset named HGeoKG-MHT-670K. Statistical analysis reveals significant regional heterogeneity and long-tail distribution patterns in HGeoKG-MHT-670K. Furthermore, extensive geographic knowledge reasoning experiments on HGeoKG-MHT-670K show that most knowledge graph embedding (KGE) models fail to achieve satisfactory performance. This suggests the need to accommodate spatial heterogeneity across different regions and improve the embedding quality of long-tail geographic entities. HGeoKG serves as both a reference for GeoKG construction and a benchmark for geographic knowledge reasoning, driving the development of geographical artificial intelligence (GeoAI).

Geographic knowledge graph representation learning embeds entities and relationships in geographic knowledge graphs into a low-dimensional continuous vector space, which serves as a basic method that bridges geographic knowledge graphs and geographic applications. Previous geographic knowledge graph representation methods primarily learn the vectors of entities and their relationships from their spatial attributes and relationships, which ignores various semantics of entities, resulting in poor embeddings on geographic knowledge graphs. This study proposes a two-stage multimodal geographic knowledge graph representation (MGKGR) model that integrates multiple kinds of semantics to improve the embedding learning of geographic knowledge graph representation. Specifically, in the first stage, a spatial feature fusion method for modality enhancement is proposed to combine the structural features of geographic knowledge graphs with two modal semantic features. In the second stage, a multi-level modality feature fusion method is proposed to integrate heterogeneous features from different modalities. By fusing the semantics of text and images, the performance of geographic knowledge graph representation is improved, providing accurate representations for downstream geographic intelligence tasks. Extensive experiments on two datasets show that the proposed MGKGR model outperforms the baselines. Moreover, the results demonstrate that integrating textual and image data into geographic knowledge graphs can effectively enhance the model’s performance.