Towards Quick Understanding and Analysis of Large-Scale Ontologies

  • Miao Xiong
  • YiFan Chen
  • Hao Zheng
  • Yong Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4185)


With the development of semantic web technologies, large and complex ontologies are constructed and applied to many practical applications. In order for users to quickly understand and acquire information from these huge information “oceans”, we propose a novel ontology visualization approach accompanied by “anatomies” of classes and properties. With the holistic “imaging”, users can both quickly locate the interesting “hot” classes or properties and understand the evolution of the ontology; with the anatomies, they can acquire more detailed information of classes or properties that is arduous to collect by browsing and navigation. Specifically, we produce the ontology’s holistic “imaging” which contains a semantic layout on classes and distributions of instances. Additionally, the evolution of the ontology is illustrated by the changes on the “imaging”. Furthermore, detailed anatomies of classes and properties, which are enhanced by techniques in database field (e.g. data mining), are ready for users.


Association Rule Voronoi Diagram Voronoi Tessellation Ontology Evolution Instance Level 
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.
    Tu, K., Xiong, M., Zhang, L., Zhu, H., Zhang, J., Yu, Y.: Towards imaging large-scale ontologies for quick understanding and analysis. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 702–715. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  3. 3.
    Noy, N.F., Klein, M.: Ontology evolution: Not the same as schema evolution. SMI technical report SMI-2002-0926 (2002)Google Scholar
  4. 4.
    Klein, M., Noy, N.F.: A component-based framework for ontology evolution. In: Workshop on Ontologies and Distributed Systems at IJCAI 2003, Acapulco, Mexico (2003)Google Scholar
  5. 5.
    Pinto, H.S.A.N.P., Martins, J.P.: Evolving ontologies in distributed and dynamic settings. In: KR 2002, pp. 365–374 (2002)Google Scholar
  6. 6.
    Galiano, F.B., Marín, N.: Data mining: Concepts and techniques - book review. SIGMOD Record. 31, 66–68 (2002)CrossRefGoogle Scholar
  7. 7.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Software - Practice and Experience 21, 1129–1164 (1991)CrossRefGoogle Scholar
  8. 8.
    Du, Q., Faber, V., Gunzburger, M.: Centroidal voronoi tessellations: Applications and algorithms 41, 637–676 (1999)Google Scholar
  9. 9.
    Balzer, M., Deussen, O., Lewerentz, C.: Voronoi treemaps for the visualization of software metrics. In: SOFTVIS 2005, pp. 165–172 (2005)Google Scholar
  10. 10.
    Johnson, B., Shneiderman, B.: Tree maps: A space-filling approach to the visualization of hierarchical information structures. In: IEEE Visualization, pp. 284–291 (1991)Google Scholar
  11. 11.
    Zhang, L., Yu, Y., Lu, J., Lin, C., Tu, K., Guo, M., Zhang, Z., Xie, G., Su, Z., Pan, Y.: ORIENT: Integrate ontology engineering into industry tooling environment. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 823–837. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Sintek, E.: OntoViz: Ontoviz tab: Visualizing protege ontologies (2003)Google Scholar
  13. 13.
    Alani, H.: TGVizTab: An ontology visualization extension for protege. In: Knowledge Capture 2003 - Workshop on Visualizing Information in Knowledge Engineering, Sanibel Island, FL (2003)Google Scholar
  14. 14.
    Storey, M.A.D., Noy, N.F., Musen, M.A., Best, C., Fergerson, R.W., Ernst, N.: Jambalaya: an interactive environment for exploring ontologies. In: IUI 2002, p. 239 (2002)Google Scholar
  15. 15.
    Perrin, D.: Prompt-viz: Ontology version comparison visualizations with treemaps. Master’s thesis, University of Victoria, BC, Canada (2004)Google Scholar
  16. 16.
    Fluit, C., Sabou, M., van Harmelen, F.: Ontology-based information visualization. In: Proceedings of Information Visualization 2002 (2002)Google Scholar
  17. 17.
    Robinson, P.N., Böhme, U., Lopez, R., Mundlos, S., Nürnberg, P.: Gene-ontology analysis reveals association of tissue-specific 50 cpg-island genes with development and embryogenesis. Human Molecular Genetics 13, 1969–1978 (2004)CrossRefGoogle Scholar
  18. 18.
    Cheng, J., Sun, S., Tracy, A., Hubbell, E., Morris, J., Valmeekam, V., Kimbrough, A., Cline, M.S., Liu, G., Shigeta, R., Kulp, D., Siani-Rose, M.A.: Netaffx gene ontology mining tool: a visual approach for microarray data analysis. Bioinformatics 20, 1462–1463 (2004)CrossRefGoogle Scholar
  19. 19.
    Andrews, K., Kienreich, W., Sabol, V., Becker, J., Droschl, G., Kappe, F., Granitzer, M., Auer, P., Tochtermann, K.: The infosky visual explorer: exploiting hierarchical structure and document similarities. Information Visualization 1, 166–181 (2002)CrossRefGoogle Scholar
  20. 20.
    Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: Websom - self-organizing maps of document collections. Neurocomputing 21, 101–117 (1998)MATHCrossRefGoogle Scholar
  21. 21.
    Chen, H.C., Schuffels, C., Orwig, R.: Internet categorization and search: A selforganizing approach. Journal of Visual Communication and Image Representation 7, 88–102 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miao Xiong
    • 1
  • YiFan Chen
    • 1
  • Hao Zheng
    • 1
  • Yong Yu
    • 1
  1. 1.APEX Data and Knowledge Management Lab, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP.R. China

Personalised recommendations