The inference of novel knowledge and new hypotheses from the current literature analysis is crucial 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 the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling 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 the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.

BioTAGME : A Comprehensive Platform for Biological Knowledge Network Analysis

Bellomo, Lorenzo;Ferragina, Paolo;
2022

Abstract

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial 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 the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling 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 the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.
2022
Settore INF/01 - Informatica
DT-hybrid; TAGME; annotation tools; knowledge graph; text mining; wikipedia
   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/11384/136483
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