An Automatic Approach to Extracting Geographic Information from Internet
An automatic approach to extract Geographic information especially represented by Points of Interest (POIs), is critical for identifying locations and provides the basis for various location-based services. Currently, geospatial data of POI are available through some open map services
(e.g., Google Maps, OpenStreetMap, etc.). However, the data supporting these services are either collected through the expensive commercial purchasing and company investment or gathered by the volunteered contribution of high uncertainty. With the rapid geospatial data growing on the Web,
we propose an automatic approach of extracting geographic information for building up POI resources based on the results obtained by the web search engines to mitigate the negative effect from the traditional means. According to the approach, we firstly put the types of POIs extracted from
Google Maps and the street names obtained from OpenStreetMap into the Google search engine, and then retrieve the potential addresses of POIs through parsing the search results. Secondly, the Google search engine is employed again with the retrieved addresses of POIs to extract the potential
place names. Finally, the Google search engine is employed for a third time with learning both the place names and the corresponding addresses to verify whether the place names are correct. The contributed output of the work is a place-name dataset. We respectively select 20 blocks in Chicago
and Houston in U.S. to execute our approach for verifying the research contribution. In the experiments, we choose Google Map that is of high data quality as the reference and compare the results with those from OpenStreet Map and Wikimapia. The final results indicate that the proposed approach
could effectively produce the place-name datasets on a par with Google Maps and outperform OpenStreet Map and Wikimapia.
Keywords: Address Extraction; Geographic Information Extraction; POI; Place-Name Dataset
Document Type: Research Article
Affiliations: 1: Associate Professor, Department of Computer Science Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India 2: B.E Student, Department of Computer Science Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India
Publication date: 01 August 2019
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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