  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.
