It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings
This addresses the need to reduce manual effort in schema matching for data integration, though it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of schema matching for data integration by proposing a two-step approach using neural embeddings, which robustly finds non-trivial correspondences compared to traditional methods.
Since data is often stored in different sources, it needs to be integrated to gather a global view that is required in order to create value and derive knowledge from it. A critical step in data integration is schema matching which aims to find semantic correspondences between elements of two schemata. In order to reduce the manual effort involved in schema matching, many solutions for the automatic determination of schema correspondences have already been developed. In this paper, we propose a novel end-to-end approach for schema matching based on neural embeddings. The main idea is to use a two-step approach consisting of a table matching step followed by an attribute matching step. In both steps we use embeddings on different levels either representing the whole table or single attributes. Our results show that our approach is able to determine correspondences in a robust and reliable way and compared to traditional schema matching approaches can find non-trivial correspondences.