Klarigi: Explanations for Semantic Groupings

Abstract

AbstractSummarySemantic annotation facilitates the use of background knowledge in analysis. This includes approaches that sort entities into groups, clusters, or assign labels or outcomes that are typically difficult to derive semantic explanations for. We introduce Klarigi, a tool that creates semantic explanations for groups of entities described by ontology terms implemented in a manner that balances multiple scoring heuristics. We demonstrate Klarigi by using it to identify characteristic terms for text-derived phenotypes of emergency admissions for two frequently conflated diagnoses, pulmonary embolism and pneumonia. Klarigi provides a universal method by which entity groups or labels can be explained semantically, and thus contributes to improved explainability of analysis methods.Availability and ImplementationKlarigi is freely available under an open source licence at http://github.com/reality/klarigi. Supplementary data is available with this article.Contactl.slater.1@bham.ac.uk