Universal representation learning of geographic knowledge graph by jointly embedding instances and ontology concepts

Geographic knowledge graphs (GeoKGs) represent semantic entities and relations as structured triplets, enabling intelligent retrieval and semantic reasoning in geospatial domains. However, existing knowledge graph embedding methods focus primarily on the instance view, overlooking ontological constraints and semantic hierarchies. This oversight creates a semantic segregation between concepts and instances, thereby limiting model performance in tasks such as relation prediction and generalisation for low-frequency entities. To address these challenges, this paper proposes a joint ontology–instance embedding framework within a unified semantic space, achieving synergistic modelling of ontological concepts and instance entities in GeoKGs. Specifically, we adopt CompoundE as the foundational model and introduce a collaborative embedding mechanism that enforces type constraints and semantic alignment through geometric transformations, including translation, rotation and scaling. Furthermore, a joint loss function is designed to simultaneously optimise the ontology hierarchy, instance structure and cross-view consistency. Experimental results demonstrate that our proposed method significantly outperforms multiple state-of-the-art baselines in ranking accuracy and generalisation capability on link prediction tasks, validating the effectiveness and necessity of integrating the ontology view into geographic knowledge representation learning.