Workflow for Domain- and Task-Sensitive Curation of Knowledge Graphs, with Use Case of DRKG

Recently, knowledge graphs have seen a significant increase in popularity in a wide variety of domains, as they provide a basis for many data-analytics and knowledge-discovery approaches. At the same time, many knowledge graphs are not immediately usable due to their format, unreadable or missing data, and inaccessibility. These issues present barriers to the exploration and use of knowledge graphs for big data analytics and knowledge discovery. In this paper we present a workflow for domain- and task-sensitive curation of large-scale knowledge graphs, and detail our experience with implementing this workflow with the biomedical knowledge graph called Drug Repurposing Knowledge Graph (DRKG). The workflow aims to address usability-related issues of real-life knowledge graphs, by performing data setup and curation that align with the needs of specific tasks and domains. Recognizing that domain experts and anticipated users of a knowledge graph provide invaluable expertise regarding the desired graph format, the proposed workflow involves them as humans-in-the-loop. We present the processes required to execute the workflow, detail our experience in the biomedical domain with the use case of DRKG, and discuss the challenges and lessons learned throughout the experience. We anticipate that the proposed workflow and experiences will be applicable to other domains, and that our workflow will enable and encourage exploration and wider use of large-scale knowledge graphs, thereby improving big data analytics.