Geospatial Knowledge Graphs (KGs) are widely used data structures that integrate rich knowledge from multi-source databases and play a crucial role in applications such as data retrieval and urban management. However, existing methods for Geospatial Knowledge Graph Completion (KGC) heavily rely on extensive labeled data and lack the ability to direct inference on new unlabeled geospatial databases and thus limiting their practical deployment. To address this limitation, this paper first formalizes a novel zero-shot transfer scenario and then proposes an innovative geospatial multimodal large language model framework capable of efficient Geospatial KGC with robust zero-shot generalization capabilities. Specifically, to enable effective direct inference under significant data discrepancies inherent in zero-shot scenarios, we introduce large language models (LLMs) into the geospatial KGC problem for the first time and redefine the multimodal data processing paradigm for geospatial LLMs. Next, to overcome the challenge that LLMs cannot directly handle geospatial data, we innovatively propose a Pretrain Geospatial Encoder that performs self-supervised pretraining exclusively on spatial data. Additionally, to integrate geospatial and textual modalities, we design an adaptation component that injects geospatial features into the LLMs and introduce a multi-task fine-tuning procedure. Lastly, to ensure robustness across multi-target domain scenarios, we present an implicit data alignment strategy based on adversarial learning. Extensive evaluations conducted on four real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of accuracy and robustness.
Chen et al. (Thu,) studied this question.