Proto-OKN Theme 2: SPIDER: Scalable Public Infrastructure for Distributed Entity Relationships

Proto-OKN 主题 2：SPIDER：分布式实体关系的可扩展公共基础设施

• 批准号: 2333849
• 负责人: Guha Jayachandran
• 金额: $ 150万
• 依托单位: Onai Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333849&HistoricalAwards=false
• 关键词: Proto; OKN; Theme; SPIDER; Scalable

The Scalable Public Infrastructure for Distributed Entity Relationships, SPIDER, is an NSF Proto-Open Knowledge Network (Proto-OKN) Fabric project that is developing processes and a technical solution for interconnecting distributed information represented in the form of knowledge graphs. By enabling automated interconnection among such graphs from different sources and fields, new insights can be rapidly obtained that would be impossible within the silo of a single domain or dataset. SPIDER will benefit society by facilitating greater access to reliable knowledge for the public and empowering researchers to discover new knowledge and obtain new insights from information across many different disciplines. SPIDER also accelerates the development of data-driven artificial intelligence (AI) models in the quest to address complex societal challenges. The data-driven analysis processes and overall technical solution provided by SPIDER are designed to leverage vast and rich existing data sources in the quest for solutions to some of society’s most pressing and complex challenges. By providing access to AI methods and deploying state-of-the-art interfaces for extensive querying of data, SPIDER facilitates integrated use of vast amounts of available data. SPIDER employs a scalable federated infrastructure for distributed knowledge graphs, enabling powerful queries, automated interconnection among data elements, training of artificial intelligence models, tracking of provenance, and computation of confidence scores. The SPIDER processes and architecture incorporate the capabilities needed by other Proto-OKN projects. The distributed knowledge graph fabric implemented by SPIDER is agnostic to graph formats, provides robust automated entity resolution and interconnection, allows subgraphs to be stored and processed in distributed fashion, presents powerful provenance tracking, enables GPU-accelerated queries to handle large subgraphs, and supports AI model training at scale. The solution also incorporates methods for utilizing trust networks to yield confidence scores on results, enabling participants to join the network with minimal barrier without the risk of polluting results with misinformation and without requiring a central oracle of truth. Furthermore, the solution allows for private data to be utilized in responding to queries while provably not leaking data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分布式实体关系可扩展公共基础设施(SPIDER)是一个NSF原型开放知识网络(Proto-OKN)结构项目，它正在开发以知识图形式表示的分布式信息互连的流程和技术解决方案。通过实现来自不同来源和领域的这些图表之间的自动互连，可以快速获得在单个域或数据集的竖井中不可能获得的新见解。SPIDER将促进公众更多地获得可靠的知识，并使研究人员能够从许多不同学科的信息中发现新知识和获得新的见解，从而造福社会。蜘蛛还加快了数据驱动的人工智能(AI)模型的开发，以寻求应对复杂的社会挑战。SPIDER提供的数据驱动的分析过程和整体技术解决方案旨在利用大量和丰富的现有数据源，寻求解决一些社会最紧迫和复杂挑战的办法。通过提供对人工智能方法的访问并部署最先进的接口来广泛查询数据，Spider促进了对海量可用数据的综合使用。SPIDER为分布式知识图采用了可扩展的联合基础设施，实现了强大的查询、数据元素之间的自动互联、人工智能模型的训练、来源跟踪和置信度分数的计算。SPIDER流程和架构包含了其他Proto-OKN项目所需的功能。由Spider实现的分布式知识图结构与图形格式无关，提供健壮的自动化实体解析和互联，允许以分布式方式存储和处理子图，提供强大的起源跟踪，使GPU加速的查询能够处理大型子图，并支持大规模AI模型训练。该解决方案还结合了利用信任网络对结果产生置信度分数的方法，使参与者能够以最小的障碍加入网络，而不会有错误信息污染结果的风险，也不需要中央真理先知。此外，该解决方案允许在响应查询时使用私人数据，同时可证明不会泄露数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。