Linguistic Conversion of Syntactic to Semantic Web Page

  • G. Nagarajan
  • K. K. Thyagarajan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


Information is knowledge. In earlier days one has to find a resource person or resource library to acquire knowledge. But today just by typing a keyword on a search engine all kind of resources are available to us. Due to this mere advancement there are trillions of information available on net. So, in this era we are in need of search engine which also search with us by understanding the semantics of given query by the user. One such design is only possible only if we provide semantic to our ordinary HTML web page. In this paper we have explained the concept of converting an HTML page to RDFS/OWL page. This technique is incorporated along with natural language technology as we have to provide the Hyponym and Meronym of the given HTML pages.


Ontology OWL RDFS Name entity recognition Probability Reasoner 


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  1. 1.
    Nagarajan, G., Thyagharajan, K.K.: A Novel Image Retrieval Approach for Semantic Web. International Journal of Computer Applications (January 2012)Google Scholar
  2. 2.
    Nagarajan, G., Thyagharajan, K.K.: A Survey on the Ethical Implications of Semantic Web Technology. Journal of Advanced Reasearch in Computer Engineering 4(1) (June 2010)Google Scholar
  3. 3.
    Minu, R.I., Thyagharajan, K.K.: Evolution of Semantic Web and Its Ontology. In: Second Conference on Digital Convergence (2009)Google Scholar
  4. 4.
    Bohring, H., Aure, S.: Mapping XML to OWL ontologies (2004)Google Scholar
  5. 5.
    Zhu, J., Uren, V., Motta Espotter, E.: Adaptive named Recognition for web browsing (2004)Google Scholar
  6. 6.
    Esmaili, K.S., Abolhassani, H.: A Categorization scheme for semantic web search engines (2005)Google Scholar
  7. 7.
    Hassanzadeh, H., Keyvanpour, M.R.: A Machine learning based analytical framework for semantic annotaion requirements. International Journal of Web and Semantic Technology (2011)Google Scholar
  8. 8.
    Abeyruwan, S.W.: Prontolearn: unsupervised lexico semantic ontology generation using probabilistic methods. These of universtiy of miami (2010)Google Scholar
  9. 9.
    Lenci, A., et al.: NLP based ontology learning from legal texts. A case study (2006)Google Scholar
  10. 10.
    Tijerino, Y.A., et al.: Towards ontology generation from tables. Kluwer academic publishers (2004)Google Scholar
  11. 11.
    Benslimane, S., et al.: Towards ontology extration from data intensive web sites: An html forms based reverse engineering approach. International Arab Journal of Information Tecnology (2006)Google Scholar
  12. 12.
    Mukhopadhyay, D., et al.: A New semantic web services to translate HTML pages to RDF. In: Int. Conference of IT (2007)Google Scholar
  13. 13.
    Hwangbo, H., et al.: Reusing of information constructed in HTML document: a conversion of HTML to OWL. In: Int. Conference on Control, Automation and Systems (2008)Google Scholar
  14. 14.
    Minu, R.I., Thyagharajan, K.K.: Automatic image classification using SVM Classifier. CiiT International Journal of Data Mining Knowledge Engineering (July 2011)Google Scholar
  15. 15.
    Minu, R.I., Thyagharajan, K.K.: Scrutinizing Video and Video Retrieval Concept. International Journal of Soft Computing & Engineering 1(5), 270–275 (2011)Google Scholar
  16. 16.
    Nagarajan, G., Thyagharajan, K.K.: A Machine learning technique for Semantic Search Engine. In: ICMOC NI University will publish in Elsevier Procedio (April 2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • G. Nagarajan
    • 1
  • K. K. Thyagarajan
    • 2
  1. 1.Sathyabama UniversityChennaiIndia
  2. 2.Dept. of Information & TechnologyRMK College of Engineering & TechnologyChennaiIndia

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