ARAGORN: Autonomous Relay Agent for Generation Of Ranked Networks

我们提出了一种用于生成分级网络的自主中继代理(Aragorn)， 它将查询知识提供者(KPS)并合成与用户指定的答案相关的答案 问题，基于作为ROBOKOP一部分开发的算法和组件[1，2] 在翻译机可行性阶段的应用。阿拉贡服务代表着下一个 生成ROBOKOP组件服务，迭代创新应对挑战 在翻译机可行性阶段暴露出来。 在这项工作的基础上，我们确定了必须解决的首要问题，以真正释放 翻译的力量。 1.ARAS必须能够有效和高效地在联合知识环境中运作。 第一代翻译器工具汇集了从中提取答案的完整数据集， 随后进行了排名。第二代工具必须能够有效地在 海量的分布式数据，需要一种新的方法。阿拉贡将采取异步行动， 将KP查询与答案的部分评分交织在一起，即时排列搜索方向的优先顺序， 以及提供响应于新探索的路径而随时间更新的早期结果 2.ARAS必须弥合数据表示和算法之间的精度不匹配 需要具体性，以及提出问题并喜欢在更抽象的层面上得到答案的用户。 生物医学科学家不会将问题作为数据库查询来提出。此外，即使是专家 当前生物医学数据库(如ROBOKOP KG或RTX)的用户需要探索和 制作查询以表达其意图的实验。阿拉贡将采用多种战略 为了消除有效提问的这一障碍，从问题库的基本维护开始， 到节点泛化、查询重写和诸如胶囊图之类的机器学习技术 网络。阿拉贡将进一步使用特定答案的元素来创建完形解释， 聚类，将内容相似的答案组合在一起，揭示了共性和 答案中的矛盾。 3.ARAS必须能够概括答案排名，以解决更广泛的问题 提法和数据类型，并解释反证。 在翻译器实施阶段，我们预计可以访问许多不同的KPS和 提供有关断言置信度的不同量化元数据的ARAS 协会的力量。迫切需要将这些数据综合成分数 任意形状的答案图，以便对答案进行筛选和优先排序，以供进一步分析或 用户消化。阿拉贡将通过提供一种新的评分算法来满足这一需求 (A)对任意有向多超图进行评分，(B)考虑异质数量 元数据；以及(C)利用关系的两极来纳入反证。 阿拉贡将提供对此功能的访问，并使用社区定义的连接到KPS API和数据模型。在此期间，阿拉贡团队为这些社区的努力做出了贡献 翻译机可行性阶段，如果获得资金，将继续与标准和 参考实现(SRI)小组、NCATS工作人员和其他获奖者继续定义 并完善数据共享和协作的方法和模型。阿拉贡军种将 在创建时考虑到协作，以便它们可以插入到更大的流水线中并 架构方面的努力。 阿拉贡战略的大部分风险由整个计划分担；作为标准化 发展，阿拉贡团队和翻译财团的其他成员将被要求 花费精力更新组件。阿拉贡将需要访问本体和相似性 我们预期的工具将由KPS或共享基础设施提供；如果这些不能实现， 阿拉贡团队将创建实现其目标所需的工具。此外，我们 假设存在可从中提取数据的完全与转换器兼容的KPS；如果 计划集体决定在ARAS中强制执行合规性，我们将利用我们的 在ROBOKOP中工作以实现必要的阿拉贡标准化组件。

We propose an Autonomous Relay Agent for Generation of Ranked Networks (ARAGORN), which will query Knowledge Providers (KPs) and synthesize answers relevant to user-specified questions, building upon algorithms and components developed as part of the ROBOKOP [1,2] application during the feasibility phase of Translator. The ARAGORN services represent the next generation of ROBOKOP component services, iterating and innovating in response to challenges exposed in the Translator feasibility phase. Based on that work, we have identified overarching issues that must be addressed to truly unleash the power of Translator. 1. ARAs must be able to operate in a federated knowledge environment effectively and efficiently. First-generation Translator tools assembled full data sets from which to extract answers, which were subsequently ranked. Second-generation tools must be able to efficiently operate on massive, distributed data, demanding a new approach. ARAGORN will act asynchronously, interleaving KP queries with partial scoring of answers, prioritizing search directions on-the-fly, and delivering early results that are updated over time in response to newly explored paths 2. ARAs must bridge the precision mismatch between data representations and algorithms that require specificity, and users who pose questions and prefer answers at a more abstract level. Biomedical scientists do not pose questions as database queries. Furthermore, even expert users of current biomedical databases such as ROBOKOP KG or RTX require exploration and experimentation to craft queries to express their intent. ARAGORN will employ multiple strategies to remove this barrier to asking questions effectively, from basic maintenance of a question library, to node generalization, query rewriting, and machine learning techniques such as capsule graph networks. ARAGORN will further use elements of specific answers to create gestalt explanations, clustering, and combining answers with similar content, revealing the commonalities and contradictions in answers. 3. ARAs must be able to generalize answer ranking to address a broader range of question formulations and data types, and to account for counterevidence. In the Translator implementation phase, we anticipate having access to many varied KPs and ARAs that provide diverse quantitative metadata regarding the confidence in assertions or strength of associations. There will be a pressing need to synthesize such data into scores for arbitrarily-shaped answer graphs, in order to filter and prioritize answers for further analysis or user digestion. ARAGORN will address this need by providing a novel scoring algorithm capable of (a) scoring arbitrary directed multi-hypergraphs, (b) accounting for heterogeneous quantitative metadata; and (c) leveraging relationship polarity to incorporate counterevidence. ARAGORN will provide access to this functionality, and connect to KPs using community-defined APIs and data models. The ARAGORN team has contributed to these community efforts during the Translator feasibility phase, and if funded will continue to work with the Standards and Reference Implementations (SRI) group, NCATS staff, and other awardees to continue to define and refine methods and models for data sharing and collaboration. The ARAGORN services will be created with collaboration in mind, such that they can be plugged into larger pipelining and architectural efforts. Most of the risks to the ARAGORN strategy are shared by the entire program; as standardization evolves, the ARAGORN team and other members of the Translator consortium will be required to spend effort updating components. ARAGORN will require access to ontology and similarity tools that we anticipate will be provided by KPs or shared infrastructure; if these do not materialize, the ARAGORN team will create the tools that it needs to accomplish its goals. Additionally, we are assuming the existence of fully translator-compliant KPs from which to draw data; if the program collectively decides that compliance is enforced in ARAs instead, we will draw on our work in ROBOKOP to implement the necessary normalization components in ARAGORN.