Toward a neural aggregated search model for semi-structured documents

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Date
2013-07
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Publisher
CERIST
Abstract
In 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.
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Keywords
Neural Networks, Self-organizing maps, Aggregated Search, XML Information Retrieval, XML document, Aggregate, Classification of XML elements, Learning
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http://dl.cerist.dz/handle/CERIST/367
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