A Multi-Feature Based Automatic Approach to Geospatial Record Linking

Ying Zhang, Puhai Yang, Chaopeng Li, Gengrui Zhang, Cheng Wang, Hui He, Xiang Hu and Zhitao Guan
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Ying Zhang: School of Control and Computer Engineering, North China Electric Power University, Beijing, China
Puhai Yang: North China Electric Power University, Beijing, China
Chaopeng Li: School of Control and Computer Engineering, North China Electric Power University, Beijing, China
Gengrui Zhang: North China Electric Power University, Beijing, China
Cheng Wang: North China Electric Power University, Beijing, China
Hui He: North China Electric Power University, Beijing, China
Xiang Hu: North China Electric Power University, Beijing, China
Zhitao Guan: North China Electric Power University, Beijing, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 4, 73-91

Abstract: This article describes how geographic information systems (GISs) can enable, enrich and enhance geospatial applications and services. Accurate calculation of the similarity among geospatial entities that belong to different data sources is of great importance for geospatial data linking. At present, most research works use the name or category of the entity to measure the similarity of geographic information. Although the geospatial relationship is significant for geographic similarity measure, it has been ignored by most of the previous works. This article introduces the geospatial relationship and topology, and proposes an approach to compute the geospatial record similarity based on multiple features including the geospatial relationships, category and name tags. In order to improve the flexibility and operability, supervised machine learning such as SVM is used for the task of classifying pairs of mapping records. The authors test their approach using three sources, namely, OpenStreetMap, Google and Wikimapia. The results showed that the proposed approach obtained high correlation with the human judgements.

Date: 2018
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