A novel approach to assess and improve syntactic interoperability in data integration

Data integration is essential to enrich a database with external information. One effective approach is to match shared identifiers across diverse databases. However, a lack of syntactic interoperability, which refers to the ability to match data based on their syntax, can pose challenges. In this paper, we present a novel method to evaluate and enhance syntactic interop-erability, considering associated costs. First, we introduce the linking index and completeness index as generic measures of fine-grained syntactic interoperability. Second, we analyze the data consistency level of the identifiers using a rule-based framework for data quality assessment. Third, we propose a data integration strategy that strikes a balance between fixing data inconsistencies and the resulting benefits, as measured by the linking and completeness indices. The approach is illustrated through two use cases: bibliographic databases and clinical trial registries. The results demonstrate that standardizing identifiers' representations can signifi-cantly improve syntactic interoperability in certain scenarios while in others, the standardization process does not yield improvements, discouraging, hence integration decisions. By conducting a cost-benefit analysis of improving data interoperability, this analysis enables data integrators to make informed decisions regarding the feasibility and advantages of proceeding with data integration.