A Novel Framework for Medical Web Information Foraging Using Hybrid ACO and Tabu Search

  • Yassine Drias
  • Samir Kechid
  • Gabriella Pasi
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Health Information Systems & Technologies


We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint.


Medical data management Information foraging Web intelligence Multi-agent systems Swarm intelligence Ant colony optimization (ACO) Tabu search Page ranking MedlinePlus 


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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.USTHB-LRIAAlgiersAlgeria
  2. 2.Universita degli Studi di Milano BicoccaMilanoItaly

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