Toward Efficient Semantic Annotation: A Semantic Cloud Generation Scheme from Linked Data

  • Han-Gyu Ko
  • In-Young Ko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8295)


As a bridge for evolution of the current Web toward Semantic Web, semantic annotation plays an important role to turn regular Web contents into meaningful ones. However, existing semantic annotation methods mostly use semantic terms in ontology created by domain experts. Therefore, they cannot cover the various subjects of contents, some of which frequently change. To deal with this problem by alternating ontology to Linked Data, we propose a semantic cloud generation scheme that finds and merges relevant terms from Linked Data for a given request. To reduce the complexity of handling a large amount of RDF data, we locate essential points at which to start searching for relevant concepts in Linked Data; we then iteratively analyze potential merges of different semantic data. In this paper, we describe the challenges of forming semantic clouds out of Linked Data and the approach of effectively generating semantic clouds by using the similarity link analysis method.


Similarity link analysis Semantic cloud generation Linked Data Semantic annotation Semantic Web 


  1. 1.
    Uren, V., Cimiano, P., Iria, J., Handschuh, S., Vargas-Vera, M., Motta, E., Ciravegna, F.: Semantic annotation for knowledge management: Requirements and a survey of the state of the art. elsevier Journal of Web Semantics (2005)Google Scholar
  2. 2.
    Reeve, L., Han, H.: Survey of Semantic Annotation Platforms. In: ACM Symposium on Applied Computing (2005)Google Scholar
  3. 3.
    Popov, B., Kiryakov, A., Kirilov, A., Manov, D., Ognyanoff, D., Goranov, M.: KIM–Semantic Annotation Platform. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 834–849. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM – Semi-automatic CREAtion of Metadata. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 358–372. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Faviki, (accessed May 8, 2013)
  6. 6.
    Mirizzi, R., Ragone, A., Di Noia, T., Di Sciascio, E.: Semantic tag cloud generation via DBpedia. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 36–48. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Lord, F.M.: Optimal Number of Choices per Item – A Comparison of Four Approaches. Journal of Educational Measurement, 14(1), 33–38 (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Han-Gyu Ko
    • 1
  • In-Young Ko
    • 1
    • 2
  1. 1.Department of Computer ScienceKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
  2. 2.Division of Web Science TechnologyKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

Personalised recommendations