Schema Matching and Schema Merging based on Uncertain Semantic Mappings

This dissertation lies in the research area of schema integration: the problem of combining the data of different data sources by creating a unified representation of these data. Two core issues in schema integration are schema matching, i.e. the identification of correspondences, or mappings, between input schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Examples of mappings found in the literature include semantic mappings, e.g. “author represents the same concept as writer”, and data mappings, e.g. “each data value of name is equal to the concatenation of a first-name value and a last-name value”. In this dissertation, we propose a schema integration framework which (1) is only concerned with semantic mappings (that associate schema objects based on simple set based comparisons of the objects’ instances) and which (2) explicitly represents and manages the uncertainty as to which semantic relationship is the correct one to use in any mapping. In our framework, we adopt a wide set of semantic mappings that allow for a precise, rigorous and formal schema merging process. Our merging process produces a sound and complete integrated schema for each pair of input schemas, and in addition it generates view definitions between the input schemas and the integrated schema.