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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Vydané v:	Journal of biomedical semantics Ročník 14; číslo 1; s. 18 - 19
Hlavní autori:	Rabby, Gollam, D’Souza, Jennifer, Oelen, Allard, Dvorackova, Lucie, Svátek, Vojtěch, Auer, Sören
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 28.11.2023 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	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
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Abstract	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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.
AbstractList	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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data. 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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data. Abstract 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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data. 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 work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data. Keywords: Influential scholarly document prediction, Machine learning algorithms, Text mining, COVID-19, World health organization, Domain-independent knowledge graph
ArticleNumber	18
Audience	Academic
Author	Svátek, Vojtěch Oelen, Allard Auer, Sören D’Souza, Jennifer Dvorackova, Lucie Rabby, Gollam
Author_xml	– sequence: 1 givenname: Gollam surname: Rabby fullname: Rabby, Gollam email: gollam.rabby@l3s.de, rabg00@vse.cz organization: L3S Research Center, Leibniz University Hannover, Department of Information and Knowledge Engineering, Prague University of Economics and Business – sequence: 2 givenname: Jennifer surname: D’Souza fullname: D’Souza, Jennifer organization: Leibniz Information Centre for Science and Technology – sequence: 3 givenname: Allard surname: Oelen fullname: Oelen, Allard organization: Leibniz Information Centre for Science and Technology – sequence: 4 givenname: Lucie surname: Dvorackova fullname: Dvorackova, Lucie organization: Department of Econometrics, Prague University of Economics and Business – sequence: 5 givenname: Vojtěch surname: Svátek fullname: Svátek, Vojtěch organization: Department of Information and Knowledge Engineering, Prague University of Economics and Business – sequence: 6 givenname: Sören surname: Auer fullname: Auer, Sören organization: L3S Research Center, Leibniz University Hannover, Leibniz Information Centre for Science and Technology
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Keywords	Text mining COVID-19 Influential scholarly document prediction Machine learning algorithms World health organization Domain-independent knowledge graph
Language	English
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Snippet	Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this... Abstract Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task....
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SubjectTerms	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
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Title	Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph
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