Semantic Retrieval of Geospatial Datasets Based on a Semantic Repository

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


In this chapter, we propose a methodology to semantically search geospatial datasets by means of their metadata. It consists of structuring a semantic repository in order to provide the inclusion mechanisms of distributed data to be retrieved, as well as the extraction of those geographic objects with respect to their conceptual similarity. The approach proposes a conceptual measure called the Conceptual Distance algorithm to determine the conceptual similarity among concepts. The features of geographic objects are conceptualized in an ontology on the domains: thematic, spatial, and temporal. The ontology is populated by using Federal Geographic Data Committee specification with instances of geographic objects distributed in several locations on a network. In the retrieval process according to a query, the objects are presented as a ranking list, which provides other results that are semantically close by means of radius of search and it avoids empty results. The approach has been implemented in a web system called SemGsearch.



This work was partially sponsored by the National Polytechnic Institute (IPN), the National Council for Science and Technology (CONACyT) under grant 106692, and Research and Postgraduate Secretary (SIP) under grants 20120563 and 20121661.


  1. Abu-Hanna A, Jansweijer WNH (1994) Modeling domain knowledge using explicit conceptualization. IEEE Expert 9(2):53–64CrossRefGoogle Scholar
  2. Amor D (2000) The e-business (r) evolution: living and working in an interconnected world. Prentice Hall PTR Upper Saddle River, NJGoogle Scholar
  3. Bishr Y (1998) Overcoming the semantic and other barriers to GIS interoperability. Int J Geogr Inf Sci 12(4):299–314CrossRefGoogle Scholar
  4. Boley H et al (2010) Integrating positional and slotted knowledge on the semantic web. J Emerg Technol Web Intell 2(4):343–353Google Scholar
  5. Buccella A et al (2007) Integration of geographic information systems. In: IX Workshop of Researchers in Computer Science, pp 397Google Scholar
  6. CFE (Mexican Federal Electricity Commission). Accessed 20 June 2012
  7. CONABIO (Mexican National Commission for Knowledge and Use of Biodiversity). Accessed 20 June 2012
  8. FGDC (Federal Geographic Data Committee), Accessed 20 June 2012
  9. Floyd RW (1962) Algorithm 97: shortest path. Commun ACM 5(6):345CrossRefGoogle Scholar
  10. Heywood I et al (2006) Geographical information systems. Pearson Education LimitedGoogle Scholar
  11. INEGI (Mexican National Institute of Statistics, Geography and Informatics) (2012). Accessed 20 June 2012
  12. Janowicz K (2007) Similarity-based retrieval for geospatial semantic web services specified using the web service modeling language (wsml-core). In: Scharl A, Tochtermann K (eds). The geospatial web-how geo-browsers, social software and the web, vol 2. Springer, London, pp 235–246Google Scholar
  13. Kiryakov A et al (2004) Semantic annotation, indexing, and retrieval. Web Semant Sci Serv Agents World Wide Web 2(1):49–79CrossRefGoogle Scholar
  14. Liang Y et al (2007) Hybrid ontology integration for distributed system. In Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. SNPD 2007, 1(1):309–314Google Scholar
  15. MacEachren AM (2004) How maps work: representation, visualization, and design. The Guilford Press, NYGoogle Scholar
  16. Malik SK et al (2010) Semantic annotation framework for intelligent information retrieval using KIM architecture. Int J Web Seman Technol 1(4):12–26CrossRefGoogle Scholar
  17. McGuinness DL et al (2004) OWL web ontology language overview. W3C recommendation, 2004–2003 (10). Accessed 20 June 2012
  18. Sabbouh M et al (2007) Web mashup scripting language. In: Proceedings of the 16th International Conference on World Wide Web, ACM, pp 1305–1306Google Scholar
  19. Santos JM et al (2003) Fuentes, tratamiento y representación de la información geográfica, “Sources, treatment and representation of the geographic information”. Universidad de educación a distancia UNED, Madrid, p 421Google Scholar
  20. Torres M et al (2011) GEONTO-MET: an approach to conceptualizing the geographic domain. Int J Geogr Inf Sci 25(10):1633–1657CrossRefGoogle Scholar
  21. Vilches LM et al (2009) Towards a semantic harmonization of geographic information. Treballs de la Societat Catalana de Geografía, pp 727–736Google Scholar
  22. Zhan Q, Zhang X, Lic D (2008) Ontology-based semantic description model for discovery and retrieval of geospatial information. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol 32Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Julio Vizcarra
    • 1
  • Miguel Torres
    • 1
  • Rolando Quintero
    • 1
  1. 1.Intelligent Processing of Geospatial Information Lab-Centre for Computing ResearchNational Polytechnic InstituteMexico CityMexico

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