A holistic approach to aligning geospatial data with multidimensional similarity measuring |
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| Authors: | Li Yu Peiyuan Qiu Xiliang Liu Bo Wan |
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| Institution: | 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People's Republic of China;2. National Science Library, Chinese Academy of Sciences, Beijing, People's Republic of China;3. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, People's Republic of China;4. Faculty of Information Engineering, China University of Geosciences, Wuhan, People's Republic of China |
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| Abstract: | 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. |
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| Keywords: | Geospatial data data alignment similarity matching semantic web |
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