IGSNRR OpenIR
Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion
Qiu, Peiyuan1; Gao, Jianliang1,2; Yu, Li3; Lu, Feng1,2,4,5
2019-06-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume8Issue:6Pages:23
Corresponding AuthorLu, Feng(luf@lreis.ac.cn)
AbstractA Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads to a situation where information extraction systems can hardly detect enough references of geographic information in the massive web resource to be able to build relatively complete GeoKGs. This incompleteness, due to missing geo-entities or geo-relations in GeoKG fact triplets, seriously impacts the performance of GeoKG applications. In this paper, a method with geospatial distance restriction is presented to optimize knowledge embedding for GeoKG completion. This method aims to encode both the semantic information and geospatial distance restriction of geo-entities and geo-relations into a continuous, low-dimensional vector space. Then, the missing facts of the GeoKG can be supplemented through vector operations. Specifically, the geospatial distance restriction is realized as the weights of the objective functions of current translation knowledge embedding models. These optimized models output the optimized representations of geo-entities and geo-relations for the GeoKG's completion. The effects of the presented method are validated with a real GeoKG. Compared with the results of the original models, the presented method improves the metric Hits@10(Filter) by an average of 6.41% for geo-entity prediction, and the Hits@1(Filter) by an average of 31.92%, for geo-relation prediction. Furthermore, the capacity of the proposed method to predict the locations of unknown entities is validated. The results show the geospatial distance restriction reduced the average error distance of prediction by between 54.43% and 57.24%. All the results support the geospatial distance restriction hiding in the GeoKG contributing to refining the embedding representations of geo-entities and geo-relations, which plays a crucial role in improving the quality of GeoKG completion.
Keywordgeographic knowledge graph geographic knowledge embedding knowledge graph completion geographic relation triplet
DOI10.3390/ijgi8060254
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41631177] ; National Natural Science Foundation of China[41801320]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000475307000012
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58101
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Feng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China
4.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Qiu, Peiyuan,Gao, Jianliang,Yu, Li,et al. Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(6):23.
APA Qiu, Peiyuan,Gao, Jianliang,Yu, Li,&Lu, Feng.(2019).Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(6),23.
MLA Qiu, Peiyuan,et al."Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.6(2019):23.
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