A holistic approach to aligning geospatial data with multidimensional similarity measuring. Issue 8 (3rd August 2018)

• Record Type: Journal Article
• Authors: Yu, Li; Qiu, Peiyuan; Liu, Xiliang; Lu, Feng; Wan, Bo
• Is Part Of: International journal of digital earth. Volume 11:Issue 8(2018)
• Page Start: 845
• Page End: 862
• Publication Date: 2018-08-03
• Subjects: Geospatial data -- data alignment -- similarity matching -- semantic web; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2017.1359688
• Languages: English
• ISSNs: 1753-8947

Abstract

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.