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Semantic Web Search System Founded on Case-Based Reasoning and Ontology Learning

  • Hajer Baazaoui-Zghal
  • Nesrine Ben Mustapha
  • Manel Elloumi-Chaabene
  • Antonio Moreno
  • David Sanchez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 272)

Abstract

With the continuous growth of data volume on the Web, the search for information has become a challenging task. Ontologies are used to improve the accuracy of information retrieval from the web by incorporating a degree of semantic analysis during the search. However, manual ontology building is time consuming. An automatic approach may aid to solve this problem by analyzing implicitly available knowledge such as the users’ search feedback. In this context, we propose a semantic web search system founded on Case-Based-Reasoning (CBR) and ontology learning that aims to enrich automatically the ontologies by using previous search queries performed by the user. Some experiments and results obtained with the proposed system are also presented, which show an improvement on the precision of the Web search and ontology enrichment.

Keywords

Semantic web search Ontologies Ontology learning Case-Based-Reasoning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hajer Baazaoui-Zghal
    • 1
  • Nesrine Ben Mustapha
    • 1
  • Manel Elloumi-Chaabene
    • 1
  • Antonio Moreno
    • 2
  • David Sanchez
    • 2
  1. 1.Riadi Lab.ENSI Campus Universitaire de la ManoubaTunisTunisie
  2. 2.ITAKA Research Group, Departament d’Enginyeria Informatica i MatematiquesUniversitat Rovira i VirgiliTarragonaSpain

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