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

Alessandro Muscolino; Antonio Di Maria; Rosaria Valentina Rapicavoli; Salvatore Alaimo; Lorenzo Bellomo; Fabrizio Billeci; Stefano Borzì; Paolo Ferragina; Alfredo Ferro; Alfredo Pulvirenti

doi:10.1007/s41109-021-00435-x

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

Appl Netw Sci. 2022;7(1):1. doi: 10.1007/s41109-021-00435-x. Epub 2022 Jan 6.

Affiliations

¹ Department of Physics and Astronomy, University of Catania, Catania, Italy.
² Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
³ Department of Computer Science, University of Pisa, Pisa, Italy.
⁴ Department of Maths and Computer Science, University of Catania, Catania, Italy.

^# Contributed equally.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s41109-021-00435-x.

Keywords: Knowledge graph; Network analysis; Text mining.