Visualization and exploration of linked data using virtual reality.

In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.

Semi-automatic translation of medicine usage data (in Dutch, free-text) from Lifelines COVID-19 questionnaires to ATC codes.

The mapping of human-entered data to codified data formats that can be analysed is a common problem across medical research and health care. To identify risk and protective factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility and coronavirus disease 2019 (COVID-19) severity, frequent questionnaires were sent out to participants of the Lifelines Cohort Study starting 30 March 2020. Because specific drugs were suspected COVID-19 risk factors, the questionnaires contained multiple-choice questions about commonly used drugs and open-ended questions to capture all other drugs used. To classify and evaluate the effects of those drugs and group participants taking similar drugs, the free-text answers needed to be translated into standard Anatomical Therapeutic Chemical (ATC) codes. This translation includes handling misspelt drug names, brand names, comments or multiple drugs listed in one line that would prevent a computer from finding these terms in a simple lookup table. In the past, the translation of free-text responses to ATC codes was time-intensive manual labour for experts. To reduce the amount of manual curation required, we developed a method for the semi-automated recoding of the free-text questionnaire responses into ATC codes suitable for further analysis. For this purpose, we built an ontology containing the Dutch drug names linked to their respective ATC code(s). In addition, we designed a semi-automated process that builds upon the Molgenis method SORTA to map the responses to ATC codes. This method can be applied to support the encoding of free-text responses to facilitate the evaluation, categorization and filtering of free-text responses. Our semi-automatic approach to coding of drugs using SORTA turned out to be more than two times faster than current manual approaches to performing this activity. Database URL https://doi.org/10.1093/database/baad019.