Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

NETME: on-the-fly knowledge network construction from biomedical literature

Bellomo, Lorenzo;Ferragina, Paolo;
2022

Abstract

Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.
2022
Settore INF/01 - Informatica
Knowledge graph; Network analysis; Text mining
   SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
   SoBigData-PlusPlus
   European Commission
   Horizon 2020 Framework Programme
   871042

   Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us
   Humane AI
   European Commission
   Horizon 2020 Framework Programme
   820437
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/136482
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