Place in Perspective: Extracting Online Information about Points of Interest

  • Ana O. Alves
  • Francisco C. Pereira
  • Filipe Rodrigues
  • João Oliveirinha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6439)


During the last few years, the amount of online descriptive information about places has reached reasonable dimensions for many cities in the world. Being such information mostly in Natural Language text, Information Extraction techniques are needed for obtaining the meaning of places that underlies these massive amounts of commonsense and user made sources. In this article, we show how we automatically label places using Information Extraction techniques applied to online resources such as Wikipedia, Yellow Pages and Yahoo!.


Noun Phrase Name Entity Recognition Place Semantic High Information Content String Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alves, A., Pereira, F.C., Biderman, A., Ratti, C.: Place enrichment by mining the web. In: Proc. of the Third European Conference on Ambient Intelligence (2009)Google Scholar
  2. 2.
    Genereux, R., Ward, L., Russell, J.: The behavioral component in the meaning of places. Journal of Environmental Psychology 3, 43–55 (1983)CrossRefGoogle Scholar
  3. 3.
    Kramer, B.: Classification of generic places: Explorations with implications for evaluation. Journal of Environmental Psychology 15, 3–22 (1995)CrossRefGoogle Scholar
  4. 4.
    Harrison, S., Dourish, P.: Re-place-ing space: the roles of place and space in collaborative systems. In: CSCW 1996, pp. 67–76. ACM Press, New York (1996)Google Scholar
  5. 5.
    Hightower, J.: From position to place. In: Proc. of the Workshop on Location-Aware Computing (2003); 10–12 part of the Ubiquitous Computing ConferenceGoogle Scholar
  6. 6.
    Lemmens, R., Deng, D.: Web 2.0 and semantic web: Clarifying the meaning of spatial features. In: Semantic Web meets Geopatial Applications, AGILE 2008 (2008)Google Scholar
  7. 7.
    Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: SIGIR, USA, pp. 103–110. ACM, New York (2007)Google Scholar
  8. 8.
    Amitay, E., Har’El, N., Sivan, R., Soffer, A.: Web-a-where: geotagging web content. In: SIGIR 2004, pp. 273–280. ACM, New York (2004)Google Scholar
  9. 9.
    Reuters, T.: Open calais api (2009)Google Scholar
  10. 10.
    TextWise: Semantic hacker api (2009)Google Scholar
  11. 11.
    Sabou, M., d’Aquin, M., Motta, E.: Exploring the semantic web as background knowledge for ontology matching. Journal of Data Semantics (2008)Google Scholar
  12. 12.
    Ramshaw, L., Marcus, M.: Text Chunking using Transformation-Based Learning. In: WVLC, Cambridge, USA (1995)Google Scholar
  13. 13.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL 2005, pp. 363–370 (2005)Google Scholar
  14. 14.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  15. 15.
    Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  16. 16.
    Mihalcea, R.: Semcor semantically tagged corpus. Technical report, CiteSeer (1998)Google Scholar
  17. 17.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks. In: IJCAI WS Inf. Integration, pp. 73–78 (2003)Google Scholar
  18. 18.
    Imagery, N., Agency, M.: Geonet names server (gns) (2007)Google Scholar
  19. 19.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: IJCAI, pp. 448–453 (1995)Google Scholar
  20. 20.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  21. 21.
    Zipf, G.K.: Selective Studies and the Principle of Relative Frequency in Language. Harvard University Press, Cambridge (1932)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ana O. Alves
    • 1
    • 2
  • Francisco C. Pereira
    • 1
  • Filipe Rodrigues
    • 1
  • João Oliveirinha
    • 1
  1. 1.CISUCUniversity of CoimbraPortugal
  2. 2.ISECCoimbra Institute of EngineeringPortugal

Personalised recommendations