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dc.contributor.authorMarcos Pablos, Samuel 
dc.contributor.authorGarcía Peñalvo, Francisco José 
dc.date.accessioned2018-11-06T12:19:04Z
dc.date.available2018-11-06T12:19:04Z
dc.date.issued2018
dc.identifier.citationMarcos-Pablos, S., & García-Peñalvo, F. J. (2019). Information retrieval methodology for aiding scientific database search. Soft Computing, doi:10.1007/s00500-018-3568-0es_ES
dc.identifier.urihttp://hdl.handle.net/10366/138828
dc.description.abstract[EN]During literature reviews, and specially when conducting systematic literature reviews (SLRs), nding and screening relevant papers during scienti c document search may involve managing and processing large amounts of unstructured text data. In those cases where the search topic is di cult to establish or has fuzzy limits, researchers require to broaden the scope of the search and, in consequence, data from retrieved scienti c publications may become huge and uncorrelated. However, through a convenient analysis of these data the researcher may be able to discover new knowledge which may be hidden within the search output, thus exploring the limits of the search and enhancing the review scope. With that aim, this paper presents an iterative methodology that applies text mining and machine learning techniques to a downloaded corpus of abstracts from scienti c databases, combining automatic processing algorithms with tools for supervised decision making in an iterative process sustained on the researchers' judgement, so as to adapt, screen and tune the search output. The paper ends showing a working example that employs a set of developed scripts that implement the di erent stages of the proposed methodologyes_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectInformation processinges_ES
dc.subjectInformation retrievales_ES
dc.subjectSystematic literature reviewes_ES
dc.subjectInformation technologyes_ES
dc.subjectText mininges_ES
dc.subjectVector Space Modeles_ES
dc.subjectSupport Vector Machinees_ES
dc.titleInformation retrieval methodology for aiding scienti c database searches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1007/s00500-018-3568-0
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International