Using Closure Tables to Enable Cross-Querying of Ontologies in Database-Driven Applications

Area of Science:

• Biomedical Informatics
• Database Management
• Ontology Engineering

Background:

• Biomedical ontologies are crucial for organizing health data.
• Querying and integrating multiple ontologies presents significant challenges.
• Existing methods for cross-ontology queries can be inefficient.

Purpose of the Study:

• To evaluate closure tables as a data structure for database applications querying biomedical ontologies.
• To demonstrate the effectiveness of closure tables in enabling cross-ontology queries.
• To improve patient cohort identification by linking different ontologies.

Main Methods:

• Implemented closure tables to augment the metadata of ICD-9 and ICD-10 ontologies within the i2b2 platform.
• Stored all paths within the ontology trees, including non-direct parent-child relationships.
• Utilized existing mappings between ontologies to facilitate cross-querying.

Main Results:

• Closure tables proved effective for database applications querying biomedical ontologies.
• The data structure successfully supported cross-querying between multiple ontologies.
• Augmented ontologies enabled researchers to identify patient populations using preferred ontologies.

Conclusions:

• Closure tables are a valuable data structure for biomedical ontology applications.
• This approach enhances the ability to perform cross-ontology queries.
• The method facilitates more flexible and precise patient cohort identification for researchers.