Skip to Main content Skip to Navigation
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.
Document type :
Conference papers
Complete list of metadatas

https://hal.inria.fr/hal-03084029
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Sunday, December 20, 2020 - 12:03:59 PM
Last modification on : Thursday, January 21, 2021 - 2:32:02 PM

File

WI_IAT_2020_on_COVID_19-cr.pdf
Files produced by the author(s)

Licence


Copyright

Identifiers

  • HAL Id : hal-03084029, version 1

Citation

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

Share

Metrics

Record views

61

Files downloads

247