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A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features

文献类型：期刊论文

作者Zeng, Xuhui3; Wang, Shu2; Zhu, Yunqiang2; Xu, Mengfei3; Zou, Zhiqiang1,3
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2022-12-01
卷号11期号:12页码:17
关键词recommendation system geospatial artificial intelligence geographical knowledge graph knowledge graph convolutional networks countryside ecological patterns
DOI10.3390/ijgi11120625
通讯作者Zou, Zhiqiang(zouzq@njupt.edu.cn)
英文摘要The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and 'cold start', since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the 'cold start' problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.
资助项目Chinese Academy of Science ; Chinese Scholarship Council[XDA23100100] ; National Natural Science Foundation of China[202008320044] ; [42050101]
WOS研究方向Computer Science ; Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000902570800001
出版者MDPI
资助机构Chinese Academy of Science ; Chinese Scholarship Council ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/188444]  
专题中国科学院地理科学与资源研究所
通讯作者Zou, Zhiqiang
作者单位1.Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210023, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Xuhui,Wang, Shu,Zhu, Yunqiang,et al. A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2022,11(12):17.
APA Zeng, Xuhui,Wang, Shu,Zhu, Yunqiang,Xu, Mengfei,&Zou, Zhiqiang.(2022).A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,11(12),17.
MLA Zeng, Xuhui,et al."A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11.12(2022):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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