MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation

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.