A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering
文献类型：期刊论文
| 作者 | Zhang, Chunju4; Chu, Chaoqun4; Zhou, Kang4; Wang, Shu1,2,3; Zhu, Yunqiang1,2,3; Huang, Jianwei4; Wu, Zhaofu4; Gao, Fei4 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2025-07-28 |
| 卷号 | 14期号:8页码:295 |
| 关键词 | geographic knowledge graph representation learning spatio-temporal evolutionary knowledge question answering |
| DOI | 10.3390/ijgi14080295 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets-GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. |
| URL标识 | 查看原文 |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001557683300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216172]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhu, Yunqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Geog Informat Sci & Technol, Beijing 100101, Peoples R China; 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210046, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 4.Hefei Univ Technol, Coll Civil Engn, Hefei 230009, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Chunju,Chu, Chaoqun,Zhou, Kang,et al. A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2025,14(8):295. |
| APA | Zhang, Chunju.,Chu, Chaoqun.,Zhou, Kang.,Wang, Shu.,Zhu, Yunqiang.,...&Gao, Fei.(2025).A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,14(8),295. |
| MLA | Zhang, Chunju,et al."A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 14.8(2025):295. |
入库方式： OAI收割
来源：地理科学与资源研究所
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