Ontologies as a Tool for Formalizing Data Validation Rules

Abstract

Comparison of health data across national or even regional boundaries is a challenging task. Data sources, data collection methods, and data quality can vary widely and the quality of the indicators themselves is dependent upon the veracity of the underlying data. For any trans-regional or trans-national comparison of indicators, it is imperative to ensure data are appropriately validated. Ontologies provide a number of functionalities to help in this process. Data rules can be formalized using the ontology axioms, which are useful for removing the ambiguities of rules expressed in natural language. In addition, the axioms serve to identify the metadata and their corresponding semantic relationships, which can in turn be linked to standard data dictionaries or other ontologies. Moreover, ontologies provide the means for encapsulating the underlying data model of the domain allowing the rules and the data model to be maintained in a single application. Finally the expression of the axioms in description logic, as supported for example by the web ontology language, allows machine reasoning to validate data sets automatically against the formalized rules.