Aligning geographic entities from historical maps for building knowledge graphs
文献类型：期刊论文
| 作者 | Sun, Kai1,2,3; Hu, Yingjie3; Song, Jia1,4; Zhu, Yunqiang1,4 |
| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
 |
| 出版日期 | 2020-11-13 |
| 页码 | 30 |
| 关键词 | Historical map geographic knowledge graph geographic entity alignment geospatial data matching map conflation |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2020.1845702 |
| 通讯作者 | Zhu, Yunqiang(zhuyq@igsnrr.ac.cn) |
| 英文摘要 | Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more time-consuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89. |
| WOS关键词 | SEMANTIC SIMILARITY ; FOREST COVER ; CONFLATION ; EXTRACTION ; REPRESENTATION ; APPROXIMATE ; INTEGRATION ; NETWORKS ; AREA |
| 资助项目 | National Natural Science Foundation of China[41771430] ; National Natural Science Foundation of China[41631177] ; University at Buffalo Research Foundation[38159749] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100100] ; China Scholarship Council[201804910732] |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000588532600001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; University at Buffalo Research Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; China Scholarship Council |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156587]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhu, Yunqiang |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.SUNY Buffalo, Dept Geog, GeoAI Lab, Buffalo, NY USA 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Kai,Hu, Yingjie,Song, Jia,et al. Aligning geographic entities from historical maps for building knowledge graphs[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2020:30. |
| APA | Sun, Kai,Hu, Yingjie,Song, Jia,&Zhu, Yunqiang.(2020).Aligning geographic entities from historical maps for building knowledge graphs.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,30. |
| MLA | Sun, Kai,et al."Aligning geographic entities from historical maps for building knowledge graphs".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020):30. |
入库方式： OAI收割
来源：地理科学与资源研究所
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