Semantic Information Retrieval on the Web

  • Ebru Sezer
  • Adnan Yazıcı
  • Ünal Yarımağan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)


In this study, a semantic information retrieval system to access web content is proposed. Web pages existing in the web contain not only textual but also visual data. When textual and visual data are combined, the semantics of the information presented in a web page becomes richer. Consequently, types of text body and visual data are queried as one entity in a single query sentence to improve the precision, recall and r norm parameters of a web query. Fuzzy domain ontology to fill the gap between raw content and semantic features is used, and a model namely OAC (Object, Action and Concept) is proposed. The core of our system is the OAC Model used for fuzzy domain ontology derivation. The OAC Model serves both images and texts, equally. Several experiments are carried out on selected real web pages, and good results are obtained.


Spatial Relation Semantic Feature Domain Ontology Query Term Action Rule 
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.


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  1. 1.
    W3C Semantic Web,
  2. 2.
    Sugmaran, V., Storey, V.C.: Ontologies for Conceptual Modeling: Their Creation, Use and Management. Data Knowledge Eng. 42 (2002)Google Scholar
  3. 3.
    Lagoze, C., Hunter, J.: The ABC Ontology Model. Journal of Digital Information, 2(2), Article No. 77 (2001)Google Scholar
  4. 4.
    Reinberger, M.-L., Spyns, P., Pretorius, A.J., Daelemans, W.: Automatic Initiation of an Ontology. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3290, pp. 600–617. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Bodner, R., Song, F.: Knowledge-based Approaches to Query Expansion in Information Retrieval. In: Proc. Of Advances in Artificial Intelligence, pp. 146–158. Springer, New York (1996)Google Scholar
  6. 6.
    Elliman, D., Rafael, J., Pulido, G.: Automatic Derivation on On-Line Document Ontology. In: Int. Work. on Mechanisms for Enterprise Integration: From Objects to Ontology (MERIT 2001) 15th Eur. Conf. on Obj. Ori. Prog. (2001)Google Scholar
  7. 7.
    Khan, L., Wang, L.: Automatic Ontology Derivation Using Clustering for Image Classification, Multimedia Information Systems, pp. 56–65 (2002)Google Scholar
  8. 8.
    Vallet, D., Miriam, F., Castells, P.: An Ontology-Based Information Retrieval Model. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 455–470. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Song, J.-f., Zhang, W.-m., Xiao, W.-d., Li, G.-h., Xu, Z.-n.: Ontology-Based Information Retrieval Model for the Semantic Web. In: IEEE Int. Conf. on e-Tech., e-Commmerce and e-Service (IEEE 2005), pp. 152–155 (2005)Google Scholar
  10. 10.
    Parry, D.: A Fuzzy Ontology For Medical Document Retrieval. In: Proc. of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalization, vol. 32, pp. 121–126 (2004)Google Scholar
  11. 11.
    Chang-Shing, L., Zhi-Wei, J.: A Fuzzy Ontology and Its Application to News Summarization. IEEE Tran. on Sys., Man and Cybernetics Part B 35(5) (2005)Google Scholar
  12. 12.
    Widyantoro, D.H., Yen, J.: A Fuzzy Ontology Based Abstract Search Engine and Its User Studies. In: IEEE Int. Fuzzy System Conference 2001 (2001)Google Scholar
  13. 13.
    The Jena Ontology Management Library,
  14. 14.
    The Apache Software Foundation,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ebru Sezer
    • 1
  • Adnan Yazıcı
    • 2
  • Ünal Yarımağan
    • 1
  1. 1.Computer Engineering DepartmentHacettepe UniversityBeytepe
  2. 2.Computer Engineering DepartmentMiddle East Technical University 

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