Dec 14th: Using lexical and structural features for quality assurance of biomedical ontologies
Channel: AI Institute at UofSC - #AIISC
Date: 2017-12-15

Olivier Bodenreider, MD, PhD
Senior Scientist, National Library of Medicine, Bethesda, Maryland, USA

Using lexical and structural features for quality assurance of biomedical ontologies -- Application to SNOMED CT

SNOMED CT is the largest clinical ontology in the world, used in over 30 countries to support clinical documentation and analytics. Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of their life cycle, but remains challenging. We focus on the detection of missing hierarchical relations in SNOMED CT, leveraging both lexical and structural. Using description logics (DL) to represent term inclusion, a DL classifier can infer missing hierarchical relations (between a given term and the term that includes it). We also identify non-lattice subgraphs in SNOMED CT as indicative of potential errors and leverage lexical patterns to propose remediation for these errors. Preliminary results indicate that these automated methods for quality assurance can facilitate the work of human editors.