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dc.creatorBorrego Díaz, Agustínes
dc.creatorDessì, Daniloes
dc.creatorHernández, Inmaes
dc.creatorOsborne, Francescoes
dc.creatorReforgiato Recupero, Diegoes
dc.creatorRuiz Cortés, Davides
dc.creatorBuscaldi, Davidees
dc.creatorMotta, Enricoes
dc.date.accessioned2023-04-17T09:18:38Z
dc.date.available2023-04-17T09:18:38Z
dc.date.issued2022-11-07
dc.identifier.citationBorrego Díaz, A., Dessì, D., Hernández, I., Osborne, F., Reforgiato Recupero, D., Ruiz Cortés, D.,...,Motta, E. (2022). Completing Scientific Facts in Knowledge Graphs of Research Concepts. IEEE Access, 10, 125867-125880. https://doi.org/10.1109/ACCESS.2022.3220241.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/144473
dc.description.abstractIn the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (e.g., tasks, methods, evaluation techniques, proteins, chemicals), their relations, and the relevant actors (e.g., researchers, organizations) and documents (e.g., articles, books). However, they are usually very partial representations of the actual research knowledge and may miss several relevant facts. In this paper, we introduce SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs. SciCheck was evaluated against other state-of-the-art approaches on seven benchmarks, yielding excellent results. Finally, we provide a real-world use case and applied SciCheck to the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically-generated open knowledge graph including 1.2M statements extracted from the 333K most cited articles in the field of Artificial Intelligence, and generated a new version of this knowledge graph with 300K additional tripleses
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades PID2019-105471RB-I00es
dc.description.sponsorshipJunta de Andalucía P18-RT-1060es
dc.description.sponsorshipJunta de Andalucía US-1380565es
dc.formatapplication/pdfes
dc.format.extent14es
dc.language.isoenges
dc.publisherIEEE Xplorees
dc.relation.ispartofIEEE Access, 10, 125867-125880.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectKnowledge graphses
dc.subjectscience of sciencees
dc.subjectknowledge graph completiones
dc.subjecttriple classificationes
dc.subjectmachine learninges
dc.subjectsemantic webes
dc.titleCompleting Scientific Facts in Knowledge Graphs of Research Conceptses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDPID2019-105471RB-I00es
dc.relation.projectIDP18-RT-1060es
dc.relation.projectIDUS-1380565es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9940925es
dc.identifier.doi10.1109/ACCESS.2022.3220241es
dc.journaltitleIEEE Accesses
dc.publication.volumen10es
dc.publication.initialPage125867es
dc.publication.endPage125880es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes
dc.contributor.funderJunta de Andalucíaes

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