Review of ontology matching approaches and challenges

Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instance-based, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.