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: 6596
Venue: SIGMOD
Year: 2023
Pagerank: 4.427232e-05
Overall Rank: 8,906 | 38.05%
DOI: 10.1145/3588947

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,083	GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases	2026	SIGMOD	4.1945683e-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
221	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.00033121824
1,966	Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study	2020	SIGMOD	9.9175408e-05

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