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TaxonomAI

2024-09-27Póster em conferência Acesso aberto
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dc.contributor.authorPontejos, Farina
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.date.accessioned2024-11-20T22:16:20Z
dc.date.available2024-11-20T22:16:20Z
dc.date.issued2024-09-27
dc.descriptionPontejos, F. (2024). TaxonomAI: Leveraging Large Language Models for Automated Taxonomy Construction [poster]. 1. Poster session presented at Data Research Meetup by MagIC, Lisbon, Portugal. --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
dc.description.abstractAutomated Taxonomy Construction techniques typically made use of clustering algorithms such as K-means and Hierarchical Clustering. With the rising popularity of Large Language Models (LLMs) however, the use of prompting as a zero-shot approach is emerging as a new field of research.en
dc.description.versionpublishersversion
dc.description.versionunpublished
dc.format.extent1
dc.format.extent1813863
dc.identifier.otherPURE: 103520287
dc.identifier.otherPURE UUID: 55eb590f-5e33-46c9-9906-7ac687276039
dc.identifier.urihttp://hdl.handle.net/10362/175558
dc.language.isoeng
dc.peerreviewedno
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.titleTaxonomAIen
dc.title.subtitleLeveraging Large Language Models for Automated Taxonomy Construction [poster]en
dc.typeconference poster
degois.publication.firstPage
degois.publication.issue1
degois.publication.lastPage
degois.publication.titleData Research Meetup by MagIC
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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