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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)

Paper ID
7582
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,374 | 28.83%
DOI
10.1145/3769796

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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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