Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this wo...

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Veröffentlicht in:	Journal of biomedical semantics Jg. 14; H. 1; S. 18 - 19
Hauptverfasser:	Rabby, Gollam, D’Souza, Jennifer, Oelen, Allard, Dvorackova, Lucie, Svátek, Vojtěch, Auer, Sören
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 28.11.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Analysis Bioinformatics Classification Combinatorial Libraries Computational Biology/Bioinformatics Computational linguistics Computer Appl. in Life Sciences COVID-19 Data mining Data Mining and Knowledge Discovery Datasets Documents Domain-independent knowledge graph Embedding Experiments Humans Influential scholarly document prediction Knowledge Knowledge representation Language Language processing Learning algorithms Machine Learning Machine learning algorithms Mathematics Mathematics and Statistics Medical research Medicine, Experimental Methods Natural language interfaces Pattern Recognition, Automated Predictions Scholarly periodicals Semantics State-of-the-art reviews Text mining World health organization Germany Text mining COVID-19 Influential scholarly document prediction Machine learning algorithms World health organization Domain-independent knowledge graph
ISSN:	2041-1480, 2041-1480
Online-Zugang:	Volltext
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