Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer.

翻译：知识图谱是表征生物医学信息的一种日益常见的数据结构。此类知识图谱能够轻松表示异构类型的信息，且存在多种用于查询和分析图谱的算法与工具。生物医学知识图谱已被广泛应用于药物重定位、药物靶点识别、药物副作用预测及临床决策支持等领域。通常，知识图谱通过整合多个不同来源的数据并集中构建而成。本文描述BioThings Explorer——一个能够查询虚拟联邦知识图谱的应用程序，该图谱源自生物医学网络服务聚合的信息。BioThings Explorer利用对每个资源输入输出接口的语义精准标注，自动化串联网络服务调用以执行多步图谱查询。由于无需维护大型集中的知识图谱，BioThing Explorer以轻量级应用形式分发，可在查询时动态检索信息。更多信息请访问https://explorer.biothings.io，代码见https://github.com/biothings/biothings_explorer。