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Research Knowledge Graphs in NFDI4DS

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dc.contributor.authorKarmakar, Saurav
dc.contributor.authorZloch, Matthäus
dc.contributor.authorLimani, Fidan
dc.contributor.authorZapilko, Benjamin
dc.contributor.authorUpadhyaya, Sharmila
dc.contributor.authorD’Souza, Jennifer
dc.contributor.authorCastro, Leyla J.
dc.contributor.authorRehm, Georg
dc.contributor.authorAckermann, Marcel R.
dc.contributor.authorSack, Harald
dc.contributor.authorBoukhers, Zeyd
dc.contributor.authorSchimmler, Sonja
dc.contributor.authorDessí, Danilo
dc.contributor.authorMutschke, Peter
dc.contributor.authorDietze, Stefan
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:17Z
dc.date.available2023-11-29T14:50:17Z
dc.date.issued2023
dc.description.abstractThe ever-increasing amount of research output through scientific articles requires means to enable transparency and a better understanding of key entities of the research lifecycle, referred to as research artifacts, such as methods, software, datasets, etc. Research Knowledge Graphs (RKG) make research artifacts findable, accessible, interoperable, and reusable (FAIR) and facilitate their interpretability. In this article, we describe the role of RKGs, from their construction to the expected benefits, including an overview and a vision of their use within the German National Research Data Infrastructure (NFDI) consortium NFDI4DataScience (NFDI4DS). This paper includes insights about the existing RKGs, how to formally represent research artifacts, and how this supports better transparency and reproducibility in data science and artificial intelligence. We also discuss key challenges, such as RKG construction, and integration, and give an outlook on future work.en
dc.identifier.doi10.18420/inf2023_102
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43022
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectNFDI
dc.subjectNFDI4DS
dc.subjectData Science
dc.subjectResearch Knowledge Graph
dc.subjectScholarly Data
dc.subjectKnowledge Graph Integration
dc.subjectKnowledge Graph Federation
dc.titleResearch Knowledge Graphs in NFDI4DSen
dc.typeText/Conference Paper
gi.citation.endPage918
gi.citation.publisherPlaceBonn
gi.citation.startPage909
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖffentliche Infrastruktur - Research Data Infrastructures for Data Science and AI (RDI4DataScience)

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten