Abstract

Semantic interoperability refers to the ability of autonomous and heterogeneous systems to exchange information with unambiguous, shared meaning that preserves context and intent across distributed platforms. In increasingly interconnected environments—spanning healthcare, e‑commerce, government services, and large‑scale scientific collaborations—traditional syntactic integration has proven inadequate for achieving true data understanding. Semantic frameworks, grounded in formal ontologies, metadata standards, knowledge representation languages, and standardized vocabularies, enable systems to interpret, integrate, and reason over distributed information sources. This research investigates the design, implementation, and evaluation of semantic interoperability frameworks, with particular emphasis on overcoming heterogeneity at structural, syntactic, and semantic levels. By reviewing established frameworks such as OWL, RDF, ISO/IEC specifications, and domain‑specific ontologies, the study highlights best practices and the limitations of current approaches. A mixed‑method research methodology, encompassing systematic literature synthesis and a case‑centric analysis of real use‑cases, reveals critical success factors, architectural patterns, and integration challenges. The findings demonstrate that while semantic frameworks significantly improve data exchange accuracy and reuse, they face issues such as ontology alignment complexity, scalability constraints, and standard adoption barriers. Recommendations are provided to guide future research and practical implementations.