The unknown knowns: a graph-based approach for temporal COVID-19 literature mining

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dc.authorid0000-0002-8150-4053en_US
dc.authorscopusid57195324891en_US
dc.authorwosidU-7459-2019en_US
dc.contributor.authorBayram, Ulya
dc.contributor.authorRoy, Runia
dc.contributor.authorAssalil, Aqil
dc.contributor.authorBenHiba, Lamia
dc.date.accessioned2023-03-14T11:12:26Z
dc.date.available2023-03-14T11:12:26Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.descriptionMÜHENDİSLİK FAKÜLTESİ ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ Scopusen_US
dc.description.abstractPurpose - The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research. Design/methodology/approach - The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered. Findings - Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions. Originality/value - To the authors' knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.en_US
dc.identifier.citationBayram, U., Roy, R., Assalil, A., & BenHiba, L. (2021). The unknown knowns: A graph-based approach for temporal COVID-19 literature mining. Online Information Review, 45(4), 687-708. doi:10.1108/OIR-12-2020-0562en_US
dc.identifier.doi10.1108/OIR-12-2020-0562
dc.identifier.endpage708en_US
dc.identifier.issn1468-4527
dc.identifier.issn1468-4535
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85102780122
dc.identifier.startpage687en_US
dc.identifier.urihttps://doi.org/10.1108/OIR-12-2020-0562
dc.identifier.urihttps://hdl.handle.net/20.500.12428/3792
dc.identifier.volume45en_US
dc.identifier.wosWOS:000631393900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBayram, Ulya
dc.language.isoen
dc.publisherEmeralden_US
dc.relation.ispartofOnline Information Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectSemantic Graphsen_US
dc.subjectNatural language Processingen_US
dc.subjectLink Predictionen_US
dc.subjectMachine Learningen_US
dc.titleThe unknown knowns: a graph-based approach for temporal COVID-19 literature mining
dc.typeArticle

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