The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
More recently, especially as researchers have shifted their focus from databases to ontologies, additional emphasis has been placed on exploiting the relationships among fields (also called concepts in the ontology context), such as is-a and has-a relationships. Because these methods are applied to expert-developed ontologies (e.g., different anatomy ontologies) there are generally only a few available instances for each field. Methods exist to leverage known matched instances for schema matching @cite_20 . Such matched pairs provide a significant advantage in finding schema matches. In many applications, including typical cross-organizational data integration efforts, the existence of common referents cannot be assumed. Furthermore, even if such common referents exist, finding them is itself a highly challenging research problem. Our method does not depend on having coreferents.
@cite_20
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A common task in many database applications is the migration of legacy data from multiple sources into a new one. This requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. Currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. In this paper, we describe QuickMig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. QuickMig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. QuickMig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. The results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.