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

分布式实体关系的可扩展公共基础设施（SPIDER）是美国国家科学基金会（NSF）的一个原型开放知识网络（Proto-OKN）结构项目，该项目正在开发用于互连以知识图形式表示的分布式信息的流程和技术解决方案。通过使来自不同来源和领域的这些图之间实现自动互连，可以快速获得新的见解，这在单个域或数据集的筒仓中是不可能的。天基信息平台将通过便利公众更多地获得可靠的知识并使研究人员能够发现新知识和从许多不同学科的信息中获得新的见解而造福社会。SPIDER还加速了数据驱动的人工智能（AI）模型的开发，以应对复杂的社会挑战。天基信息平台提供的数据驱动分析流程和总体技术解决方案旨在利用现有的大量丰富的数据源，寻求解决社会上一些最紧迫和最复杂的挑战的办法。通过提供对人工智能方法的访问并部署最先进的接口来进行广泛的数据查询，SPIDER促进了大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邦基础设施，实现了强大的查询、数据元素之间的自动互连、人工智能模型的训练、来源跟踪和置信度得分的计算。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对图形格式不可知，提供强大的自动化实体解析和互连，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来产生结果置信度分数的方法，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，也不需要一个中央真理预言机。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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.