PT  - JOURNAL ARTICLE
AU  - Luke T Slater
AU  - John A Williams
AU  - Andreas Karwath
AU  - Sophie Russell
AU  - Samantha C Pendleton
AU  - Hilary Fanning
AU  - Simon Ball
AU  - Paul Schofield
AU  - Robert Hoehndorf
AU  - Georgios V Gkoutos
TI  - Klarigi: Explanations for Semantic Groupings
AID  - 10.1101/2021.06.14.448423
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.14.448423
4099  - http://biorxiv.org/content/early/2021/06/15/2021.06.14.448423.short
4100  - http://biorxiv.org/content/early/2021/06/15/2021.06.14.448423.full
AB  - Summary Semantic 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 Implementation Klarigi is freely available under an open source licence at http://github.com/reality/klarigi. Supplementary data is available with this article.Contact l.slater.1{at}bham.ac.ukCompeting Interest StatementThe authors have declared no competing interest.