Schema matching is a fundamental problem in database applications such as data integration, e-business, data warehousing, and semantic query processing. Currently, it is typically performed manually, which is time-consuming and error-prone. Previous research has proposed techniques to automate schema matching for specific domains. This paper presents a taxonomy of existing approaches, distinguishing between schema-level, instance-level, element-level, structure-level, language-based, and constraint-based matchers. It reviews previous implementations to indicate which parts of the solution space they cover. The taxonomy and review are intended to help compare approaches, develop new algorithms, and implement schema matching components. Schema matching is essential in various application domains. In schema integration, schemas from different domains or developed by different people must be merged into a coherent schema. In data warehouses, schemas from multiple sources are integrated into a single warehouse. In e-commerce, message translation between different formats is needed. In semantic query processing, user queries must be mapped to database schema elements. The match operator takes two schemas as input and produces a mapping between corresponding elements. It is crucial for designing transformations, merging schemas, and composing mappings. The paper discusses various applications of schema matching, including schema integration, data warehouses, e-commerce, and semantic query processing. It also describes a high-level architecture for implementing the match operator and classifies different ways to perform it automatically. The paper concludes with a literature review and a discussion of future research directions.Schema matching is a fundamental problem in database applications such as data integration, e-business, data warehousing, and semantic query processing. Currently, it is typically performed manually, which is time-consuming and error-prone. Previous research has proposed techniques to automate schema matching for specific domains. This paper presents a taxonomy of existing approaches, distinguishing between schema-level, instance-level, element-level, structure-level, language-based, and constraint-based matchers. It reviews previous implementations to indicate which parts of the solution space they cover. The taxonomy and review are intended to help compare approaches, develop new algorithms, and implement schema matching components. Schema matching is essential in various application domains. In schema integration, schemas from different domains or developed by different people must be merged into a coherent schema. In data warehouses, schemas from multiple sources are integrated into a single warehouse. In e-commerce, message translation between different formats is needed. In semantic query processing, user queries must be mapped to database schema elements. The match operator takes two schemas as input and produces a mapping between corresponding elements. It is crucial for designing transformations, merging schemas, and composing mappings. The paper discusses various applications of schema matching, including schema integration, data warehouses, e-commerce, and semantic query processing. It also describes a high-level architecture for implementing the match operator and classifies different ways to perform it automatically. The paper concludes with a literature review and a discussion of future research directions.