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Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach
Chen, Zugang1,2,3; Song, Jia1,2; Yang, Yaping1,2,4
2018-03-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume7Issue:3Pages:19
Corresponding AuthorYang, Yaping(yangyp@igsnrr.ac.cn)
AbstractTo help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear.
Keywordartificial neural networks geospatial data similarity metadata intrinsic characteristics combination
DOI10.3390/ijgi7030090
WOS KeywordGEOGRAPHIC INFORMATION-RETRIEVAL ; SEMANTIC SIMILARITY ; SYSTEM ; AGREEMENT
Indexed BySCI
Language英语
Funding ProjectBranch Center Project of Geography, Resources and Ecology of Knowledge Center for Chinese Engineering Sciences and Technology[CKCEST-2017-1-8] ; National Earth System Science Data Sharing Infrastructure[2005DKA32300] ; Multidisciplinary Joint Scientific Expedition Project in International Economic Corridor Across China, Mongolia and Russia[2017FY101300] ; Construction Project of Ecological Risk Assessment and Basic Geographic Information Database of International Economic Corridor Across China, Mongolia and Russia[131A11KYSB20160091] ; National Natural Science Foundation of China[41631177]
Funding OrganizationBranch Center Project of Geography, Resources and Ecology of Knowledge Center for Chinese Engineering Sciences and Technology ; National Earth System Science Data Sharing Infrastructure ; Multidisciplinary Joint Scientific Expedition Project in International Economic Corridor Across China, Mongolia and Russia ; Construction Project of Ecological Risk Assessment and Basic Geographic Information Database of International Economic Corridor Across China, Mongolia and Russia ; National Natural Science Foundation of China
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000428557700011
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54957
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Yaping
Affiliation1.State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Jiang Su Ctr Collaborat Innovat Geog Informat Res, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Chen, Zugang,Song, Jia,Yang, Yaping. Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2018,7(3):19.
APA Chen, Zugang,Song, Jia,&Yang, Yaping.(2018).Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,7(3),19.
MLA Chen, Zugang,et al."Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 7.3(2018):19.
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