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

arXiv: https://arxiv.org/abs/2203.04366 · HTML: https://mlsanity.com/paper/2203.04366/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Problems With Evaluation of Word Embeddings Using Word Similarity Tasks](https://mlsanity.com/paper/1605.02276/) (2016)
- [Learning Semantic Annotations for Tabular Data](https://mlsanity.com/paper/1906.00781/) (2019)
- [Schema Matching using Machine Learning](https://mlsanity.com/paper/1911.11543/) (2019)

## Built on by (most cited first, 4 of 4)

- [GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security](https://mlsanity.com/paper/2406.01876/) (2024)
- [RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching](https://mlsanity.com/paper/2606.07843/) (2026)
- [Features matching using natural language processing](https://mlsanity.com/paper/2303.12804/) (2023)
- [Xling: A Learned Filter Framework for Accelerating High-Dimensional Approximate Similarity Join](https://mlsanity.com/paper/2402.13397/) (2024)

## Abstract (arXiv)

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.