GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases
Summary: GeoKGM: multimodal LLM framework for zero-shot geospatial KG completion—self-supervised geospatial encoder plus spatial-feature injection into LLMs and multi-task fine-tuning to enable direct inference on unlabeled geospatial DBs. Uses adversarial implicit alignment for cross-domain robustness and outperforms prior SOTA on four real-world datasets. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Zhihan Zheng (Beijing Institute of Technology)
- 2. Haitao Yuan (Nanyang Technological University)
- 3. Minxiao Chen (Beijing Institute of Technology)
- 4. Nan Jiang (Nanyang Technological University)
- 5. Haoning Wang (National University of Singapore)
- 6. Shangguang Wang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{zheng_sigmod26,
title = {{GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases}},
author = {Zheng, Zhihan and Yuan, Haitao and Chen, Minxiao and Jiang, Nan and Wang, Haoning and Wang, Shangguang},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769796},
url = {https://dl.acm.org/doi/10.1145/3769796},
year = {2026}
}
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