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Since data is often stored in different sources, it needs to be integrated togather a global view that is required in order to create value and deriveknowledge from it. A critical step in data integration is schema matching whichaims to find semantic correspondences between elements of two schemata. Inorder to reduce the manual effort involved in schema matching, many solutionsfor the automatic determination of schema correspondences have already beendeveloped.  In this paper, we propose a novel end-to-end approach for schema matchingbased on neural embeddings. The main idea is to use a two-step approachconsisting of a table matching step followed by an attribute matching step. Inboth steps we use embeddings on different levels either representing the wholetable or single attributes. Our results show that our approach is able todetermine correspondences in a robust and reliable way and compared totraditional schema matching approaches can find non-trivial correspondences.

It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings