Spatial Proximity Relations-Driven Semantic Representation for Geospatial Entity Categories
Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge...
Saved in:
| Main Authors: | Yongbin Tan, Hong Wang, Rongfeng Cai, Lingling Gao, Zhonghai Yu, Xin Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/14/6/233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Entity Profiling in Knowledge Graphs
by: Xiang Zhang, et al.
Published: (2020-01-01) -
Dual Context Representation Learning Framework for Entity Alignment
by: Bo Cheng, et al.
Published: (2025-04-01) -
THE POSSIBILITY OF CONVENTIONAL REPRESENTATION OF A CREDITOR LEGAL ENTITY BY ANOTHER LEGAL REPRESENTATIVE IN THE ENFORCEMENT PHASE
by: Emilian-Constantin MEIU
Published: (2017-05-01) -
RRDGNN: Relational reflective disentangled graph neural network for entity alignment
by: Xinchen Shi, et al.
Published: (2025-05-01) -
Representing the Spatiotemporal State Evolution of Geographic Entities as a Multi-Level Graph
by: Feng Yuan, et al.
Published: (2025-06-01)