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Manhattan Based Hybrid Semantic Similarity Algorithm for Geospatial Ontologies

  • K. Saruladha
  • E. Thirumagal
  • J. Arthi
  • G. Aghila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8279)

Abstract

The interest on the geo-spatial information system is increasing swiftly, which leads to the development of the competent information retrieval system. Among the several semantic similarity models, the existing models such as Geometric Model characterizes the geo-spatial concept using their dimensions (i.e. properties) and the Network Model, using their spatial relations which has yielded less precision. For retrieving the geo-spatial information efficiently, the dimensions and the spatial relations between the geo-spatial concepts must be considered. Hence this paper proposes the Hybrid Model which is the concoction of the Geometric Model’s dimensions and the Network Model’s relations using the Manhattan distance method for computing semantic distance between geo-spatial query concept and the related geo-spatial concept in the data sources. The results and analysis illustrates that the Hybrid Model using Manhattan distance method could yield better precision, recall and the relevant information retrieval. Further the Manhattan Based Similarity Measure (MBSM) algorithm is proposed which uses the Manhattan Distance Method for computing the semantic similarity among the geo-spatial concepts which yields 10% increase in precision compared to the existing semantic similarity models.

Keywords

Geospatial information retrieval Hybrid Model Euclidean distance Manhattan distance Dimensions Spatial relations Conceptual contexts 

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References

  1. 1.
    Win, K.K.: Measuring Geospatial Semantic Similarity between Geospatial Entity Classes. IEEE, University of Computer Studies (2006)Google Scholar
  2. 2.
    Schwering, A.: Hybrid model for semantic similarity measurement. In: 4th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE 2005), pp. 1449–1465. Springer, Agia Napa (2005)Google Scholar
  3. 3.
    Schwering, A., Raubal, M.: Measuring semantic similarity between geospatial conceptual regions. In: Rodríguez, M.A., Cruz, I., Levashkin, S., Egenhofer, M. (eds.) GeoS 2005. LNCS, vol. 3799, pp. 90–106. Springer, Heidelberg (2005a)CrossRefGoogle Scholar
  4. 4.
    Hosseinabady, M., Nunez-Yanez: Run-time stochastic task mapping on a large scale network-on-chip with dynamically reconfigurable tiles. J.L. Computers & Digital Techniques, IET 6(1), 1–11 (2012), doi:10.1049/iet-cdt.2010.0097CrossRefGoogle Scholar
  5. 5.
    Gärdenfors, P.: How to make the Semantic Web more semantic Formal Ontology in Information Systems. In: Vieu, L. (ed.) Proceedings of the Third International Conference (FOIS 2004), vol. 114, pp. 153–164. IOS Press (2004)Google Scholar
  6. 6.
    Rodríguez, A., Egenhofer, M.J.: Comparing geospatial entity classes: An asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science 18(3), 229–256 (2004)CrossRefGoogle Scholar
  7. 7.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  8. 8.
    Goldstone, R.L., Son, J.: Similarity Cambridge Handbook of Thinking and Reasoning. K. Holyoak and R. Morrison, pp. 13–36. Cambridge University Press, Cambridge (2005)Google Scholar
  9. 9.
    Levenshtein, I.V.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)MathSciNetGoogle Scholar
  10. 10.
    Vadivel, A., Majumdar, A.K., Sural, S.: Performance comparison of distance metrics in content-based Image retrieval applications. Computer Science and Engineering, Indian Institute of Technology, Kharagpur, vadi@cc.iitkgp.ernet.inGoogle Scholar
  11. 11.
    Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  12. 12.
    Jain, A., Vailaya, A.: Image Retrieval using Color and Shape. Pattern Recognition 29(8), 1233–1244 (1996)CrossRefGoogle Scholar
  13. 13.
    Sural, S., Qian, G., Pramanik, S.: Segmentation and Histogram Generation using the HSV Color Space for Content Based Image Retrieval. In: IEEE Int. Conf. on Image Processing, Rochester, NY (2002)Google Scholar
  14. 14.
    Sural, S.: Histogram Generation from the HSV Color Space using Saturation Projection. In: Multimedia Systems and Content-based Image Retrieval. Idea Group Publishing, Hershey (2003) (in press)Google Scholar
  15. 15.
    Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Pektovic, D., Yanker, P., Faloutsos, C., Taubin, G.: The QBIC Project: Querying Images by Content using Color Texture and Shape. In: Proc. SPIE Int. Soc. Opt. Eng., in Storage and Retrieval for Image and Video Databases, vol. 1908, pp. 173–187 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • K. Saruladha
    • 1
  • E. Thirumagal
    • 1
  • J. Arthi
    • 1
  • G. Aghila
    • 2
  1. 1.Department of Computer Science and EngineeringPondicherry Engineering CollegePuducherryIndia
  2. 2.Department of Computer Science and EngineeringPondicherry UniversityPuducherryIndia

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