Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 142, 2019
https://doi.org/10.5194/ica-proc-2-142-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 142, 2019
https://doi.org/10.5194/ica-proc-2-142-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

Research on Deep Web POI Acquisition based on Retrieving Word Optimization and Spatial Adaption

Yong Wang, An Luo, Jiping Liu, and Yuanhui Cao Yong Wang et al.
  • Chinese Academy of Surveying and Mapping; No. 28 Lianhuachi West Road, Haidian District, Beijing100830, China

Keywords: Deep web POI, data collection, retrieving word optimization, spatial adaptive subdivision

Abstract. POI data is a geographical information resource that is the most closely related to the public life, and has been successfully applied in various fields such as urban planning, urban logistics, and car navigation. With the development of technologies such as mobile networks and Internet of Things, the network contains a large number of high-value POI information resources. How to effectively acquire and utilize data resources has become a research hotspot in the field of spatial information. In this paper, a deep-web POI information search method based on independent coverage ranking and spatial adaptive partition is proposed to solve the problems of difficult construction of retrieval word base and limited data request. By constructing candidate search terms, searching greedily, optimizing dimensionality reduction of search terms, and crawling spatially adaptive partitioning, the maximum coverage optimal solution of POI search is approached step by step, and the full POI information of deep web is obtained. It is of great significance to improve the recall rate and collection efficiency of POI data for enriching geographic information resources and improving the ability of spatial information service and content management.

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