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)
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 |
|---|---|---|---|---|
| 10,374 | GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases | 2026 | SIGMOD | 5.093636e-05 |
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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 |
|---|---|---|---|---|
| 141 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.0002964847 |
| 2,781 | Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study | 2020 | SIGMOD | 8.1281896e-05 |
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