Data Interoperability with Ontology Alignment Strategies across Biomedical Terminology Repositories

Abstract

The exponential growth of biomedical data has necessitated the development of robust terminology repositories to facilitate seamless data integration and interoperability. However, the diverse structural and semantic properties of these repositories often hinder effective cross-platform data exchange. This paper investigates the predictability of data interoperability outcomes based on ontology alignment strategies, utilizing advanced graph analysis techniques. By conceptualizing biomedical terminologies as complex network graphs, we systematically evaluate how different alignment strategies impact the topological characteristics of the integrated knowledge spaces. The study extracts structural features from ontology graphs, including degree distribution, centrality measures, and modularity, to model the underlying alignment dynamics. We propose a comprehensive methodological framework that employs graph-based predictive modeling to forecast the success of semantic interoperability between distinct clinical repositories. Through extensive empirical analysis of large-scale biomedical ontologies, our findings indicate that specific graph-theoretic metrics strongly correlate with high-fidelity data integration. Furthermore, predictive models leveraging these network features demonstrate significant accuracy in estimating interoperability performance prior to the execution of computationally expensive alignment processes. This research provides a foundational understanding of the relationship between network topology and semantic alignment, offering strategic guidance for optimizing biomedical data integration workflows.