BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis - Di Maria, Antonio; Alaimo, Salvatore; Bellomo, Lorenzo; Billeci, Fabrizio; Ferragina, Paolo; Ferro, Alfredo; Pulvirenti, Alfredo (Frontiers in Genetics, 2022)
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.