Browsing by Author "Boughanem, Mohand"

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    Experiments on Page Rank Algorithm in the XML Information Retrieval Context
    (2009) Mataoui, Mhamed; Boughanem, Mohand; Mezghiche, Mohamed
    In this paper we present two adaptations of the PageRank algorithm to collections of XML documents and the experimental results obtained for the Wikipedia collection used at INEX-1 2007. These adaptations to which we referred as ldquoDOCRANK and TOPICAL_docrankrdquo allow the re-rank of the results returned by the base run execution to improve retrieval quality. Our experiments are performed on the results returned by the three best ranked systems in the ldquoFocusedrdquo task of INEX 2007. Evaluations have shown improvements in the quality of retrieval results (improvement of some topics is very significant, eg: topic 491, topic 521, etc.). The best improvement achieved in the results returned by the DALIAN2 university system (global rate obtained for the 107 topics of INEX 2007) was about 3.78%
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    Exploiting Link Evidence to Improve XML Information Retrieval
    (2010) Mataoui, Mhamed; Mezghiche, Mohamed; Boughanem, Mohand
    In this paper, we examine how link evidence can be exploited in XML information retrieval (XML IR) field. We experimented some well-known link analysis algorithms, i.e., PageRank, HITS and SALSA, in XML IR context. We propose to re-rank XML elements by combining their topical score with their link score computed according to one of the above algorithms. These experiments, performed on the Wikipedia collection provided by INEX, showed that the use of links in topical context improves the retrieval accuracy
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    Ré-ordonnancement contextuel des résultats en RI structurée
    (2007) Mataoui, Mhamed; Boughanem, Mohand
    La réinjection de pertinence est un processus connu en RI. Il consiste à corriger le processus de recherche en fonction de jugements utilisateur. La réinjection de la pertinence pourrait intervenir à deux niveaux : reformuler la requête ou bien réordonner les résultats renvoyés. Notre contribution consiste à proposer une nouvelle approche (dite ré-ordonnancement contextuel) permettant de réordonner la liste des résultats renvoyée par le système de recherche en se basant sur les jugements utilisateur afin d'en améliorer la qualité. Nos évaluations ont été effectuées sur des données renvoyées par le système XFIRM et montrent de bonnes performances par rapport à l'exécution de base