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

Aug 5, 2026

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Multiple studies have explored bibliometric features and uncategorized scholarly documents for influential scholarly document prediction. This work advances beyond bibliometric metadata by examining the task across categorized documents. A new representation method combines machine learning text representations with a domain-independent knowledge graph (DBpedia), enriching document features using RDF type and unqualified relations. Experiments on the WHO COVID-19 corpus compare TF-IDF, bag-of-words, and BERT. TF-IDF performs best; logistic regression supports category classification, while random forest improves influential prediction with the knowledge graph, especially for categorical data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

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Abstract

Introduction

Text classification and prediction background

Motivation for influential scholarly document prediction

Research questions

Rabby etal. Journal of Biomedical Semantics (2023) 14:18 [https://doi.org/10.1186/s13326-023-00298-4](https://doi.org/10.1186/s13326-023-00298-4) Journal of Biomedical Semantics RESEARCH Open Access Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph Gollam Rabby1,2*, Jennifer D’Souza3, Allard Oelen3, Lucie Dvorackova4, Vojtěch Svátek2 and Sören Auer1,3 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 ofCOVID-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 *Correspondence: Gollam Rabby [gollam.rabby@l3s.de](gollam.rabby@l3s.de); [rabg00@vse.cz](rabg00@vse.cz) Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativecom](http://creativecom)[mons.org/publicdomain/zero/1.0/](mons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a c

FAQs

How does the domain-independent knowledge graph contribute to the method?

DBpedia enriches document representation through direct type (RDF type) and unqualified relations, improving influential document prediction for categorical data.

Aug 5, 2026

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