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dc.contributor.authorPerez Riaño, Alain
dc.contributor.authorBasagoiti, Rosa
dc.contributor.authorLarrinaga, Felix
dc.contributor.otherCortez, Ronny Adalberto
dc.contributor.otherBarrasa, Ekaitz
dc.contributor.otherUrrutia, Ainara
dc.date.accessioned2022-07-06T09:32:48Z
dc.date.available2022-07-06T09:32:48Z
dc.date.issued2018
dc.identifier.issn0169-023Xen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=148176en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5626
dc.description.abstractTechnology Watch human agents have to read many documents in order to manually categorize and dispatch them to the correct expert, that will later add valued information to each document. In this two step process, the first one, the categorization of documents, is time consuming and relies on the knowledge of a human categorizer agent. It does not add direct valued information to the process that will be provided in the second step, when the document is revised by the correct expert. This paper proposes Machine Learning tools and techniques to learn from the manually pre-categorized data to automatically classify new content. For this work a real industrial context was considered. Text from original documents, text from added value information and Semantic Annotations of those texts were used to generate different models, considering manually pre-established categories. Moreover, three algorithms from different approaches were used to generate the models. Finally, the results obtained were compared to select the best model in terms of accuracy and also on the reduction of the amount of document readings (human workload).en
dc.description.sponsorshipKonikeres
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2018 Elsevier B.V. All rights reserved.en
dc.subjectText miningen
dc.subjectKnowledge management applicationsen
dc.subjectMulti-classificationen
dc.subjectTechnology watch automationen
dc.subjectSemantic annotationsen
dc.titleA case study on the use of machine learning techniques for supporting technology watchen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceData and Knowledge Engineeringen
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedfalseen
local.description.publicationfirstpage239en
local.description.publicationlastpage251en
local.identifier.doihttps://doi.org/10.1016/j.datak.2018.08.001en
local.contributor.otherinstitutionKoniker S. Koopes
local.contributor.otherinstitutionhttps://ror.org/01jmr1174es
local.source.detailsVol. 117. Pp. 239-251. September, 2018en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_71e4c1898caa6e32en


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Registro sencillo