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
{"title":"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","authors":"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","doi":"10.3233/sw-222974","DOIUrl":null,"url":null,"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.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"240 1","pages":"697-727"},"PeriodicalIF":3.0000,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science\",\"authors\":\"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\",\"doi\":\"10.3233/sw-222974\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":48694,\"journal\":{\"name\":\"Semantic Web\",\"volume\":\"240 1\",\"pages\":\"697-727\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2022-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Semantic Web\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/sw-222974\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Semantic Web","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/sw-222974","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science
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.
Semantic WebCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
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.