Title |
Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph / Gollam Rabby, Jennifer D’Souza, Allard Oelen, Lucie Dvorackova, Vojtěch Svátek, Sören Auer |
|---|---|
Involved |
Gollam Rabby (Verfasser)
Jennifer D’Souza (Verfasser) Allard Oelen (Verfasser) Lucie Dvorackova (Verfasser)
Vojtěch Svátek (Verfasser)
Sören Auer (Verfasser) |
Published |
Hannover: Gottfried Wilhelm Leibniz Universität Hannover |
Extent |
Online-Ressource |
Language |
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Country |
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Topic |
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Subject |
COVID-19
Domain-independent knowledge graph Influential scholarly document prediction Machine learning algorithms
Text mining
World health organization |
DDC notation |
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Persistent identifier |
urn:nbn:de:101:1-2024031401231541883416 (URN) |
Further information |
In: Rabby, G.; D’Souza, J.; Oelen, A.; Dvorackova, L.; Svátek, V. et al.: Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph. In: Journal of Biomedical Semantics 14 (2023), 18. DOI: https://doi.org/10.1186/s13326-023-00298-4 |
Record ID |
1323402403 |
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