AUTHOR=Di Maria Antonio , Alaimo Salvatore , Bellomo Lorenzo , Billeci Fabrizio , Ferragina Paolo , Ferro Alfredo , Pulvirenti Alfredo 

TITLE=BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.855739

DOI=10.3389/fgene.2022.855739

ISSN=1664-8021

ABSTRACT=The inference of novel knowledge and generation of new hypotheses from the current literature analysis is a crucial process in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic  gain of knowledge concerning relationships among biological elements, in form of semantically related terms (or entities), is rising novel research challenges and corresponding applications.   
In this regard, we propose BioTAGME, a system which combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). The aim of this integration is to create a large Knowledge Graph modelling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through Laravel framework in connection with Neo4j graph database to store the knowledge graph.