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

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Semantic Web Pub Date : 2022-08-30 DOI:10.3233/sw-222974

M. Sy, Bogdan Roman, Samuel Kerrien, Didac Montero Mendez, Henry Genet, Wojciech Wajerowicz, Michaël Dupont, Ian Lavriushev, Julien Machon, Kenneth Pirman, Dhanesh Neela Mana, Natalia Stafeeva, Anna-Kristin Kaufmann, Huanxiang Lu, Jonathan Lurie, Pierre-Alexandre Fonta, Alejandra Garcia Rojas Martinez, Alexander Ulbrich, Carolina Lindqvist, Silvia Jimenez, D. Rotenberg, H. Markram, Sean L. Hill

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引用次数: 2

Abstract

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.

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蓝脑Nexus:一个开放、安全、可扩展的知识图谱管理和数据驱动科学系统

现代数据驱动的科学通常由数据发现、获取、准备、分析、模型构建和验证的迭代周期组成，从而导致知识发现和大规模传播。在瑞士EPFL蓝脑项目(BBP)中，构建和模拟整个啮齿动物大脑的独特挑战需要一个解决方案来管理大规模高度异构的数据，并跟踪它们的来源，以确保在这些迭代周期中的质量、可重复性和归属。在这里，我们描述了蓝脑Nexus (BBN)，这是一个开源、领域不可知、可扩展、可扩展的数据和知识图谱管理系统的生态系统，由BBP建立，以应对这些挑战。BBN建立在开放标准和可互操作的语义web技术之上，能够创建和管理经W3C SHACL验证的基于rdf的安全知识图谱。BBN支持一系列(元)数据建模和表示格式，包括JSON和JSON- ld，以及更正式指定的基于sha的模式，这些模式支持域模型驱动的运行时API。凭借其基于流事件的架构，BBN支持异步构建和维护多个可扩展索引，以确保高性能搜索功能并启用分析。我们提出了四个用例和BBN在计算建模、神经科学、精神病学和开放关联数据方面的大规模数据集成和传播挑战的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

8.30

自引率

6.70%

发文量

期刊介绍： The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.