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Author: search.filters.author.Bessai, Fatma-ZohraSubject: search.filters.subject.Possibilistic network

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    Possibilistic Networks for Aggregated Search in XML Documents
    (CERIST, 2011-04) Bessai, Fatma-Zohra
    In this paper, we are interested in aggregated search in conten-based XML information retrieval. Our goal is to revisit the granularity of the unit to be returned. More precisely, instead of returning the whole document or a list of disjoint elements of a document, we attempt to build the best element aggregation (set of non redundant elements) which is likely to be relevant to a query. For this, we present a model for XML information retrieval, based on possibilistic networks. The network structure provides a natural manner to represent the links between, a document, its elements and its content, and allows automatically selecting relevant and complementary elements. Experiments carried out on a sub-collection of INEX (INitiative for the Evaluation of XML retrieval), showed the effectiveness of the approach.
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    Possibility and necessity measures for relevance assessment
    (ACM, 2007-11-09) Bessai, Fatma-Zohra; Boughanem, M.
    The major question raised in information retrieval on semi-structured documents relates to the manner of effectively handling the structure and the contents of the document for better answering the user's needs. These needs can be formulated by queries composed of only key words or key words and structural constraints. In this paper, we are interested in Information Retrieval in semi-structured document like XML. For these purposes, we present a model for the semi-structured information retrieval, based on the possibilistic networks. The document - elements and elements - terms relations are modelled by measures of possibility and necessity. In this model, the user's query starts a process of propagation to recover documents or portions of documents necessarily or at least possibly relevant. An example of such a research is proposed in order to illustrate the presented approach.