Mining Geospatial Relationships from Text

Summary: GTMiner jointly models geospatial and textual signals to construct a geospatial KG from real-world databases. Three modules—Candidate Selection, Relation Prediction, KG Refinement—enable efficient, accurate mining of geospatial relations; cross-city tests show improved KG coverage and competitive training/inference times. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hc1b3f5a616e878bc
Venue: SIGMOD
Year: 2023
Pagerank: 5.2056825e-05
Overall Rank: 9,239 | 37.89%
DOI: 10.1145/3588947

Incoming Non-self Citations Over Time

Authors

1. Pasquale Balsebre (Nanyang Technological University)
2. Dezhong Yao (Huazhong University of Science and Technology)
3. Gao Cong (Nanyang Technological University)
4. Weiming Huang (Nanyang Technological University)
5. Zhen Hai (Alibaba)

BibTeX Citation

@inproceedings{balsebre_sigmod23,
        title = {{Mining Geospatial Relationships from Text}},
        author = {Balsebre, Pasquale and Yao, Dezhong and Cong, Gao and Huang, Weiming and Hai, Zhen},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588947},
        url = {https://dl.acm.org/doi/10.1145/3588947},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,572	GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases	2026	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
134	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.00030043481
2,795	Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study	2020	SIGMOD	8.0014963e-05

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