A holistic approach to aligning geospatial data with multidimensional similarity measuring
作者机构：State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China National Science LibraryChinese Academy of SciencesBeijingPeople’s Republic of China Fujian Collaborative Innovation Center for Big Data Applications in GovernmentsFuzhouPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingPeople’s Republic of China Faculty of Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2018年第11卷第8期
页 面：845-862页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the National Natural Science Foundation of China[grant number 41631177] the Chinese Academy of Sciences Key Project[grant number ZDRW-ZS-2016-6-3]
主 题：Geospatial data data alignment similarity matching semantic web
摘 要：Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching ***,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD *** solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)***,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval *** presented approach is validated with real geographical semantic webs,Geonames and *** with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric *** reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.
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