Large-scale, Exhaustive Lattice-based Structural Auditing of SNOMED CT

One criterion for the well-formedness of ontologies is that their hierarchical structure forms a lattice. For-mal Concept Analysis (FCA) has been used as a tech-nique for assessing the quality of ontologies, but is not scalable to large ontologies such as SNOMED CT (>300k concepts). We developed a methodology called Lattice-based Structural Auditing (LaSA), for auditing biomedical ontologies, implemented through automated SPARQL queries, in order to exhaustively identify all non-lattice pairs in SNOMED CT. The percentage of non-lattice pairs ranges from 0 to 1.66 among the 19 SNOMED CT hierarchies. Preliminary manual inspec-tion of a limited portion of the over 544k non-lattice pairs, among over 356 million candidate pairs, revealed inconsistent use of precoordination in SNOMED CT, but also a number of false positives. Our results are con-sistent with those based on FCA, with the advantage that the LaSA pipeline is scalable and applicable to ontolog-ical systems consisting mostly of taxonomic links.