A Theoretical and Empirical Investigation of Semantic Interoperability Challenges in Heterogeneous Data Integration Architectures

Abstract

This study investigates the semantic interoperability issues that arise in heterogeneous data integration architectures, emphasizing both theoretical frameworks and empirical evidence. As data ecosystems increasingly involve disparate formats, ontologies, and schemas, achieving seamless semantic integration has become a critical challenge. The paper examines key issues such as ontology mismatches, semantic heterogeneity, and context ambiguity. By analyzing prior literature and incorporating architectural modeling, this work identifies the core impediments to interoperability and outlines strategies for enhancing semantic mediation. Findings reveal that context-aware ontologies, mediation frameworks, and machine learning-enhanced mappings are essential for overcoming semantic barriers in diverse data environments.