Using closure tables to enable cross-querying of ontologies in database-driven applications

Abstract

We demonstrate that closure tables are an effective data structure for developing database-driven applications that query biomedical ontologies and that require cross-querying between multiple ontologies. A closure table stores all available paths within a tree, even those without a direct parent-child relationship; additionally, a node can have multiple ancestors which gives the foundation for supporting linkages between controlled ontologies. We augment the meta-data structure of the ICD9 and ICD10 ontologies included in i2b2, an open source query tool for identifying patient cohorts, to utilize a closure table. We describe our experiences in incorporating existing mappings between ontologies to enable clinical and health researchers to identify patient populations using the ontology that best matches their preference and expertise.