Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Semantic web

article

Mohameth François Sy¹  Bogdan Roman¹  Samuel Kerrien¹  Didac Montero Mendez¹  Henry Genet¹  Wojciech Wajerowicz¹  Michaël Dupont¹  Ian Lavriushev¹  Julien Machon¹  Kenneth Pirman¹  Dhanesh Neela Mana¹  Natalia Stafeeva¹  Anna-Kristin Kaufmann¹  Huanxiang Lu¹  Jonathan Lurie¹  Pierre-Alexandre Fonta¹  Alejandra Garcia Rojas Martinez¹  Alexander D. Ulbrich¹  Carolina Lindqvist¹  Silvia Jimenez¹  David Rotenberg²  Henry Markram¹  Sean L. Hill¹ 

[1]Blue Brain Project, École polytechnique fédérale de Lausanne ,(EPFL), Biotech Campus

[2]Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health

[3]Department of Psychiatry – Neuroscience and Clinical Translation, University of Toronto

关键词: Knowledge graph;    Data science;    Data management;    Distributed system;    Data-driven science;   

DOI  :  10.3233/SW-222974

来源: IOS Press

【 摘 要 】

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simulating the whole rodent brain in the Swiss EPFL Blue Brain Project (BBP) required a solution to managing large-scale highly heterogeneous data, and tracking their provenance to ensure quality, reproducibility and attribution throughout these iterative cycles. Here, we describe Blue Brain Nexus (BBN), an ecosystem of open source, domain agnostic, scalable, extensible data and knowledge graph management systems built by BBP to address these challenges. BBN builds on open standards and interoperable semantic web technologies to enable the creation and management of secure RDF-based knowledge graphs validated by W3C SHACL. BBN supports a spectrum of (meta)data modeling and representation formats including JSON and JSON-LD as well as more formally specified SHACL-based schemas enabling domain model-driven runtime API. With its streaming event-based architecture, BBN supports asynchronous building and maintenance of multiple extensible indices to ensure high performance search capabilities and enable analytics. We present four use cases and applications of BBN to large-scale data integration and dissemination challenges in computational modeling, neuroscience, psychiatry and open linked data.

【 授权许可】

Unknown