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.

本文提出了一种基于神经嵌入的端到端模式匹配新方法，该方法分为表匹配和属性匹配两步。实验结果表明，该方法能够以精确可靠的方式确定匹配，并且相较于传统模式匹配方法能找到非常规匹配点。

AI匹配：使用嵌入的两步方法进行模式匹配