Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface

Abstract

Background Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. Objective We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. Methods We developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. Results We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. Conclusions TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Knowledge to Span Biomedical Data Repositories

“…Chief among these are ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways) (Bizon et al ., 2019), a biomedical knowledge graph-based, open-question-answering system that allows users to ask questions such as, “ What chemical entities are associated with lung cancer? ” and TranQL (Translator Query Language; https://tranql.renci.org) (Cox et al ., 2020), a query, visualization, and API environment for iterative querying of Translator knowledge graphs.…”

“…Briefly, the core data structure for concept expansion is a knowledge graph, in which biomedical data are organized into a network structure, with nodes representing entity types (e.g., disease, gene, chemical exposure) and edges providing predicates that describe the relationship between entities; Biolink predicates include terms such as 'causes,' 'is associated with,' and 'is expressed https://tranql.renci.org) (Cox et al, 2020), a query, visualization, and API environment for iterative querying of Translator knowledge graphs.…”

“…To contextualize metadata within a knowledge graph, we leverage data integration approaches developed through the NCATS Biomedical Data Translator (Biomedical Data Translator Consortium, 2019). Chief among these are ROBOKOP (Bizon et al, 2019) and TranQL (Translator Query Language; https://tranql.renci.org) (Cox et al, 2020). The "answers" returned by TranQL queries are used to increase the search relevance of related concepts and to provide a basis for including explanations for links that led to the result (e.g., peer reviewed literature supporting knowledge graph links).…”

“…Chief among these are ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways)(Bizon et al, 2019), a biomedical knowledge graph-based, open-question-answering system that allows users to ask questions such as, "What chemical entities are associated with lung cancer?" and TranQL (Translator Query Language; https://tranql.renci.org)(Cox et al, 2020), a query, visualization, and API environment for iterative querying of Translator knowledge graphs.Dug leverages TranQL to gather an expanded set of ontological concepts related to those extracted via NLP annotation in the previous step. Dug allows platform administrators to define the set of TranQL query templates used to retrieve related ontology identifiers.…”

An Approach for Open Multivariate Analysis of Integrated Clinical and Environmental Exposures Data

“…Moreover, as part of the Translator program, we have used ICEES to conduct a multi-institutional study, free of regulatory constraints, over the course of a five-day 'hackathon' [10]. We also have developed a tool for visualizing and exploring ICEES as a 'knowledge graph' of interconnected nodes [11].…”