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Conference papers

Mining RDF Data of COVID-19 Scientific Literature for Interesting Association Rules

Lucie Cadorel 1 Andrea G. B. Tettamanzi 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In the context of the global effort to study, understand, and fight the new Coronavirus, prompted by the publication of a rich, reusable linked data containing named entities mentioned in the COVID-19 Open Research Dataset, a large corpus of scientific articles related to coronaviruses, we propose a method to discover interesting association rules from an RDF knowledge graph, by combining clustering, community detection, and dimensionality reduction, as well as criteria for filtering the discovered association rules in order to keep only the most interesting rules. Our results demonstrate the effectiveness and scalability of the proposed method and suggest several possible uses of the discovered rules, including (i) curating the knowledge graph by detecting errors, (ii) finding relevant and coherent collections of scientific articles, and (iii) suggesting novel hypotheses to biomedical researchers for further investigation.
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Conference papers
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Submitted on : Sunday, December 20, 2020 - 12:03:59 PM
Last modification on : Friday, January 21, 2022 - 3:12:22 AM
Long-term archiving on: : Sunday, March 21, 2021 - 6:07:04 PM


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  • HAL Id : hal-03084029, version 1


Lucie Cadorel, Andrea G. B. Tettamanzi. Mining RDF Data of COVID-19 Scientific Literature for Interesting Association Rules. WI-IAT'20 - IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Dec 2020, Melbourne, Australia. ⟨hal-03084029⟩



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