A Common Dialect for Infrastructure and Services in Translator

1)我们将建立一个强大的，动态的翻译标准和参考实施 组件（SRI），该组件集成了NCATS翻译人员 所做的一切该组件将包括一套标准和产品， 它们的治理以及协调一体化和共同实施的进程： * 将在社区的支持下发展社区治理协调，以确保 一个有效的协作环境，并推动整个联盟的共识， 件. ●架构和API规范将推动社区努力定义项目细节 跨翻译知识提供者（KPs）的架构和通信协议， 自治中继代理（ARAs）和自治中继系统（ARS）。 ● BioLink模型将定义标准实体类型、关系类型和模式 由所有Translator组件共享。这包括相关的实用程序库和一个新颖的 方法来适应多个备用数据建模视角。 ●集成参考本体将提供符合BioLink的术语和关系。我们 将借鉴ROBOKOP Ubergraph框架[1]，Monarch集成本体， 开放生物学和生物医学本体（Open Biological and Biomedical Ontologies，OBO） ●持续更新的知识图谱和数据湖将为Translator提供 标准化和综合的全球信息景观。 ●下一代共享翻译服务将集成ROBOKOP的功能[3]， Monarch [4]、BioLink [5]和reasoner API来消除集成障碍。这些 服务将提供验证、查找和映射功能，以供跨Translator使用。 翻译人员KP、ARA和共享服务的注册将提高效率， 消除重复劳动，促进协作。 2)我们提出的SRI将解决连接不同组件的问题， 大规模的数据/信息来源，社区的支持，以及可持续性计划。 3)对于SRI标准部分的开发，我们的计划将开始为 我们将与ARS、ARA和KP合作，找出差距。 我们将有一个社区流程来为标准做出贡献，利用GitHub pull 请求和投票，以帮助每个人都有效和公平地做出贡献，并明确归属。我们 将确保严格的文档和测试。对于参考实现组件， 我们将支持核心翻译服务，并将包括额外的服务，如果他们是有用的， 多个转换器组件，而不是仅由一个组件使用。 4)建筑业很难。我们的团队拥有经过验证的专业知识和资源来确定需求， 完善解决方案并达成一致，从而成功地实现基础设施建设。我们 团队还拥有设计和测试必要标准的技术和生物专业知识， 一直处于多个本体、数据标准和大型企业软件的最前沿 举措 5)翻译器基础设施本质上是异构的、分布式的和不断增长的; 因此，最重要的数据和基础设施挑战是管理有效性， 货币、等价性和实体的类型（疾病、表型、药物等）。我们集团 开发了几种创新的算法来管理这个和相关的问题;这些 算法用于其他集成项目，并将进行修改，以适应翻译的需要。

1) We will build a robust, dynamic Translator Standards and Reference Implementations Component (SRI) that integrates the collaborations and investments that the NCATS Translator has made to date. This component will consist of a suite of standards and products, a model for their governance, and processes to coordinate integration and shared implementation: ● Community governance coordination will be developed with community buy-in to ensure an effective collaborative environment, and drive consortium-wide consensus on the other components. ● Architecture and API specifications will drive community efforts to define details of project architecture and communication protocols across Translator Knowledge Providers (KPs), Autonomous Relay Agents (ARAs), and the Autonomous Relay System (ARS). ● The BioLink model will define the standard entity types, relationship types, and a schema shared by all Translator components. This includes related utility libraries and a novel approach to accommodate multiple alternate data modeling perspectives. ● Integrated reference ontologies will provide BioLink-compliant terms and relationships. We will draw on the ROBOKOP Ubergraph framework [1] , the Monarch integrated ontologies, and other ontologies from Open Biological and Biomedical Ontologies (OBO) [2] . ● A continually-updated knowledge graph and data lake will provide Translator with a standardized and integrated global view of the whole information landscape. ● Next-generation Shared Translator Services will integrate features of ROBOKOP [3] , Monarch [4] , BioLink [5] , and the reasoner APIs to remove integration barriers. These services will provide validation, lookup, and mapping functionality for use across Translator. ● A registry of Translator KPs, ARAs, and shared services will increase efficiency, eliminate duplication of effort, and promote collaboration. 2) Our proposed SRI will address the problem of connecting together different components and data/information sources at scale, with community buy-in, and with a plan for sustainability. 3) For the development of the standards component of the SRI, our plan will begin with accepted Translator standards, and we will work with the ARS, ARAs, and KPs to identify gaps. We will have a community process for contributing to the standards, making use of GitHub pull requests and voting, to help everyone contribute effectively and fairly with clear attribution. We will ensure rigorous documentation and testing. For the reference implementation component, we will stand up core Translator services, and will include additional services if they are useful to more than one Translator component rather than used by only one. 4) Consensus-building is hard. Our team has proven expertise and resources to identify needs, refine solutions, and find agreement , thereby successfully bringing infrastructure to fruition. Our team also has the technical and biological expertise to design and test the necessary standards, having been at the forefront of multiple ontology, data standards, and large enterprise software initiatives. 5) The Translator infrastructure is by nature heterogeneous, distributed, and growing; consequently, the most significant data and infrastructure challenge is managing the validity, currency, equivalency, and typing of entities (diseases, phenotypes, drugs, etc.). Our group has developed several innovative algorithms for managing this and related problems; these algorithms are in use for other integration projects and will be modified to suit Translator needs.