Academic Journal
Region-adaptive representation learning for geographic knowledge graph.
| Τίτλος: | Region-adaptive representation learning for geographic knowledge graph. |
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| Συγγραφείς: | Yao H; School of Computer Science, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China; State Key Laboratory of Geomicrobiology and Environmental Changes, Wuhan, 430074, China. Electronic address: yaohong@cug.edu.cn., Gao L; School of Computer Science, China University of Geosciences, Wuhan, 430074, China. Electronic address: li.gao@cug.edu.cn., Chen R; School of Computer Science, China University of Geosciences, Wuhan, 430074, China. Electronic address: cryao@cug.edu.cn., Xie Z; School of Computer Science, China University of Geosciences, Wuhan, 430074, China. Electronic address: xiezi@cug.edu.cn., Li X; School of Computer Science, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China; State Key Laboratory of Geomicrobiology and Environmental Changes, Wuhan, 430074, China. Electronic address: lixinchuan@cug.edu.cn., Song H; State Key Laboratory of Geomicrobiology and Environmental Changes, Wuhan, 430074, China. Electronic address: haijunsong@cug.edu.cn., Li S; School of Computer Science, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China; State Key Laboratory of Geomicrobiology and Environmental Changes, Wuhan, 430074, China. Electronic address: swli@cug.edu.cn. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2027 Jan; Vol. 205 (Pt A), pp. 109414. Date of Electronic Publication: 2026 Jul 18. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Representation Machine Learning* , Artificial Intelligence*, Geography ; Algorithms |
| Περίληψη: | Geographic knowledge graph (GeoKG) organizes the geographical entities and their relationships, which provides rich spatial semantic to serve various geographic artificial intelligence tasks by knowledge graph representation learning (KGRL). As a bridge between the knowledge graph and downstream tasks, KGRL results in a speedup of inference by embedding the entities and relationships of KG into a low-dimensional vector space. However, the existing KGRLs treated subgraphs in different regions uniformly, ignoring the spatial heterogeneity of GeoKG. It may result in significant performance differences across regions. To address this problem, this study proposes a region adaptive KGRL (RA-KGRL) method to mitigate the impact of spatial heterogeneity on performance. Specifically, RA-KGRL first splits the original GeoKG into subgraphs by introducing regional priors. Then, RA-KGRL learns local models for the subgraphs and performs local-to-global optimization to learn accurate global model. Extensive experiments on thirteen datasets indicate that RA-KGRL is comparable or even better than baselines on the traditional metrics, and outperforms all baselines on the new region adaptive metrics. This study provides a methodological reference for improving the performance of KGRLs on spatial heterogeneity scenarios. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Geographic knowledge graph; Meta-learning; Representation learning; Spatial heterogeneity |
| Entry Date(s): | Date Created: 20260723 Date Completed: 20260922 Latest Revision: 20260922 |
| Update Code: | 20260923 |
| DOI: | 10.1016/j.neunet.2026.109414 |
| PMID: | 42492102 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.109414 |