A Map-Based Web Search Interface Using Point of Interest Aggregation

  • Kwangsoon Jung
  • Sangchul Ahn
  • Heedong Ko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)


With advent of a mobile computing, the pattern of information search has been changed. Search queries through mobile devices increase; 30% of Google’s organic search queries come from mobile devices, and local search, which seeks information with geospatial constraints, also increases. As of 2013 local search on mobile phones continues to grow up to 60% since 2010. However, a large number of web documents cannot be exposed to local search even though they refer to a point of interest (POI) just because they are not explicitly geo-tagged. We are interested in connecting typical web documents to spatial search based on POIs by geotagging web documents. In this paper, we present a map-based web search system that serves geospatial search queries for non-geotagged documents. The proposed system provides with fine-grained local search for typical web pages mentioning several POIs and supports semantic search in accordance with their spatial relation of inclusion.


Point of interest map search interface named-entity recognition toponym resolution entity linking local search 


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  1. 1.
    Amitay, E., Har’El, N., Sivan, R., Soffer, A.: Web-a-where: geotagging web content. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 273–280. ACM, Sheffield (2004)Google Scholar
  2. 2.
    Lieberman, M.D., Samet, H., Sankaranarayanan, J., Sperling, J.: STEWARD: architecture of a spatio-textual search engine. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, pp. 1–8. ACM, Seattle (2007)Google Scholar
  3. 3.
    Vaid, S., Jones, C.B., Joho, H., Sanderson, M.: Spatio-textual indexing for geographical search on the web. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 218–235. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Teitler, B.E., Lieberman, M.D., Panozzo, D., Sankaranarayanan, J., Samet, H., Sperling, J.: NewsStand: a new view on news. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10. ACM, Irvine (2008)Google Scholar
  5. 5.
    Wood, J., Dykes, J., Slingsby, A., Clarke, K.: Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. IEEE Transactions on Visualization and Computer Graphics 13, 1176–1183 (2007)CrossRefGoogle Scholar
  6. 6.
    Slingsby, A., Dykes, J., Wood, J., Clarke, K.: Interactive Tag Maps and Tag Clouds for the Multiscale Exploration of Large Spatio-temporal Datasets. In: 11th International Conference on Information Visualization, IV 2007, pp. 497–504 (2007)Google Scholar
  7. 7.
    Lieberman, M.D., Samet, H., Sankaranarayanan, J.: Geotagging with local lexicons to build indexes for textually-specified spatial data. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE), pp. 201–212 (2010)Google Scholar
  8. 8.
    Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774. ACM, Beijing (2011)Google Scholar
  9. 9.
    Adelfio, M.D., Samet, H.: Structured toponym resolution using combined hierarchical place categories. In: Proceedings of the 7th Workshop on Geographic Information Retrieval, pp. 49–56. ACM, Orlando (2013)CrossRefGoogle Scholar
  10. 10.
    Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications (2006)Google Scholar
  11. 11.
    Göbel, F., Jagers, A.: Random walks on graphs. Stochastic processes and their applications 2, 311–336 (1974)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    M Digital Marketing Report, Q3 2013 (2013)Google Scholar
  13. 13.
    6th Annual 15miles/Neustar Localeze Local Search Usage Study Conducted by comScore (2013)Google Scholar
  14. 14.
    Kim, K.M., Oh, H.S., Lee, K.W., Kim, E.Y., Myaeng, S.H.: A Disambiguation Method for Point of Interest Detection. Journal of KIISE (in Korean)Google Scholar
  15. 15.
    Qin, T., Xiao, R., Fang, L., Xie, X., Zhang, L.: An efficient location extraction algorithm by leveraging web contextual information. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 53–60. ACM, San Jose (2010)Google Scholar
  16. 16.
    Ahern, S., Naaman, M., Nair, R., Yang, J.H.-I.: World explorer: visualizing aggregate data from unstructured text in geo-referenced collections. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 1–10. ACM, Vancouver (2007)Google Scholar
  17. 17.
    Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: Proceedings of the 15th International Conference on Multimedia, pp. 631–640. ACM, Augsburg (2007)CrossRefGoogle Scholar
  18. 18.
    Hiramatsu, K., Kobayashi, K., Benjamin, B., Ishida, T.: Akahani, J.-i.: Map-based user interface for Digital City Kyoto. The Internet Global Summit, INET2000 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kwangsoon Jung
    • 1
  • Sangchul Ahn
    • 1
  • Heedong Ko
    • 1
    • 2
  1. 1.Department of HCI and RoboticsUniversity of Science and TechnologyDaejeonSouth Korea
  2. 2.Imaging Media Research CenterKorea Institute of Science and TechnologySeoulSouth Korea

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