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A holistic approach to aligning geospatial data with multidimensional similarity measuring 被引量:4

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摘要 Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,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 alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared 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 aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.
出处 《International Journal of Digital Earth》 SCIE EI 2018年第8期845-862,共18页 国际数字地球学报(英文)
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].
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