Toward a neural aggregated search model for semi-structured documents

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dc.contributor.authorBessai, Fatma-Zohra
dc.date.accessioned2013-11-25T14:59:29Z
dc.date.available2013-11-25T14:59:29Z
dc.date.issued2013-07
dc.description.abstractIn this paper, we are interested in content-oriented 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, as it is usually done in the most XML information retrieval systems, we attempt to build the best elements aggregation (set of non-redundant elements) which is likely to be relevant to a query composed of key words. Our approach is based on Kohonen self-organizing maps. Kohonen self-organizing map allows an automatic classification of XML elements producing density map that form the foundations of aggregated search.fr_FR
dc.identifier.isrnCERIST-DTISI/RR--13-000000021--dzfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/367
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureSystèmes et Documents Multimédia Structurés (SDMS)fr_FR
dc.subjectNeural Networksfr_FR
dc.subjectSelf-organizing mapsfr_FR
dc.subjectAggregated Searchfr_FR
dc.subjectXML Information Retrievalfr_FR
dc.subjectXML documentfr_FR
dc.subjectAggregatefr_FR
dc.subjectClassification of XML elementsfr_FR
dc.subjectLearningfr_FR
dc.titleToward a neural aggregated search model for semi-structured documentsfr_FR
dc.typeTechnical Report
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