MSEN-GRP: A Geographic Relations Prediction Model Based on Multi-Layer Similarity Enhanced Networks for Geographic Relations Completion
文献类型：期刊论文
| 作者 | Huang, Zongcai2,5; Qiu, Peiyuan1; Yu, Li4; Lu, Feng2,3,5 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 11期号:9页码:20 |
| 关键词 | geographic knowledge graph relation completion representation learning similarity network relation prediction |
| DOI | 10.3390/ijgi11090493 |
| 通讯作者 | Lu, Feng(luf@lreis.ac.cn) |
| 英文摘要 | Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity between geographic entities (geo-entities). If there is no proper choice in the method of external semantic enhancement, this will often interfere with the representation and learning of the KG. Therefore, we here propose a geographic relation (geo-relation) prediction model based on multi-layer similarity enhanced networks for geo-relations completion (MSEN-GRP). The MSEN-GRP comprises three parts: enhancer, encoder, and decoder. The enhancer constructs semantic, spatial, structural, and attribute-similarity networks for geo-entities, which can explicitly and effectively enhance the implicit semantic associations between existing geo-entities. The encoder can obtain the long path relation dependency characteristics of geo-entities using a mixed-path sampling strategy and can support different optimization schemes for external semantic enhancement. Geo-relations prediction experiments show that the mean reciprocal ranking of this method is significantly higher than those of the traditional TransE DisMult and methods, and Hits@10 is improved by up to 57.57%. Furthermore, the spatial-similarity network has the most significant enhancement effect on geo-relations prediction. The proposed method provides a new way to perform relation completion in sparse GeoKGs. |
| 资助项目 | National Natural Science Foundation of China[41631177] ; National Natural Science Foundation of China[41801320] ; National Natural Science Foundation of China[42001341] |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000858306900001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/184760]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lu, Feng |
| 作者单位 | 1.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Peoples R China 4.Beijing Inst Technol, Natl Acad Safety & Dev, Beijing 100081, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Zongcai,Qiu, Peiyuan,Yu, Li,et al. MSEN-GRP: A Geographic Relations Prediction Model Based on Multi-Layer Similarity Enhanced Networks for Geographic Relations Completion[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2022,11(9):20. |
| APA | Huang, Zongcai,Qiu, Peiyuan,Yu, Li,&Lu, Feng.(2022).MSEN-GRP: A Geographic Relations Prediction Model Based on Multi-Layer Similarity Enhanced Networks for Geographic Relations Completion.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,11(9),20. |
| MLA | Huang, Zongcai,et al."MSEN-GRP: A Geographic Relations Prediction Model Based on Multi-Layer Similarity Enhanced Networks for Geographic Relations Completion".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11.9(2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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