GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relations
文献类型：期刊论文
| 作者 | Hu, Lei1,2,3; Li, Wenwen1; Xu, Jun3; Zhu, Yunqiang3 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-10-15 |
| 卷号 | N/A |
| 关键词 | Spatial relation natural language knowledge graph embedding joint training |
| DOI | 10.1080/13658816.2024.2412731 |
| 产权排序 | 1 |
| 英文摘要 | Natural-language spatial relations between geographic entities (geoentities) reflect diverse perceptions influenced by factors like location, culture, and linguistic conventions. These relations play a crucial role in supporting geospatial tasks, such as question answering and cognitive reasoning. While prior studies focused on a limited set of human-selected spatial terms and geometric attributes, they often overlooked essential semantic attributes. To overcome this limitation, we developed a Spatial Relation-based Knowledge Graph Embedding framework, SR-KGE, with new KG fusion functions to predict spatial relation terms among distinct geoentities. This method not only considers graph structures and the diversity of natural language expressions in the embedding and learning process, but also incorporates geoentity types as a constraint to capture spatial and semantic relations more accurately. Our experiments on two knowledge graph datasets, one small-scale and one large-scale, have both shown its superior performance in spatial relation inference compared to popular KGE models, including TransE, RotatE, and HAKE. We hope our research will advance the classic study of natural language described spatial relations in a more automated and intelligent way. |
| WOS关键词 | METRIC DETAILS ; OBJECTS |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| WOS记录号 | WOS:001333015400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/208207]  |
| 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 | Xu, Jun |
| 作者单位 | 1.Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Lei,Li, Wenwen,Xu, Jun,et al. GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relations[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A. |
| APA | Hu, Lei,Li, Wenwen,Xu, Jun,&Zhu, Yunqiang.(2024).GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relations.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A. |
| MLA | Hu, Lei,et al."GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relations".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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