Ontology-Driven Solutions for Resolving Semantic Conflicts in Data Integration

Abstract

Semantic conflicts present significant challenges in data integration, arising from disparities in schema structures, terminologies, and domain interpretations. These conflicts hinder the interoperability of heterogeneous data sources, impacting data quality and decision-making processes. Ontology-driven approaches offer a structured methodology for resolving these conflicts by formalizing domain knowledge into machine-readable representations. This paper explores ontology-based frameworks for semantic conflict resolution, proposing a comprehensive methodology supported by case studies in healthcare and e-commerce. It highlights these approaches' advantages, limitations, and future directions, providing theoretical insights and empirical evaluations. This work contributes to the broader understanding of semantic integration, emphasizing ontologies' transformative role in enabling robust data ecosystems.