# Schema Matching using Machine Learning

arXiv: https://arxiv.org/abs/1911.11543 · HTML: https://mlsanity.com/paper/1911.11543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

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

- [It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings](https://mlsanity.com/paper/2203.04366/) (2022)
- [GRAM: Generative Retrieval Augmented Matching of Data Schemas in the Context of Data Security](https://mlsanity.com/paper/2406.01876/) (2024)
- [Deep Learning to Jointly Schema Match, Impute, and Transform Databases](https://mlsanity.com/paper/2207.03536/) (2022)
- [SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment](https://mlsanity.com/paper/2305.13634/) (2023)

## Abstract (arXiv)

Schema Matching is a method of finding attributes that are either similar to each other linguistically or represent the same information. In this project, we take a hybrid approach at solving this problem by making use of both the provided data and the schema name to perform one to one schema matching and introduce the creation of a global dictionary to achieve one to many schema matching. We experiment with two methods of one to one matching and compare both based on their F-scores, precision, and recall. We also compare our method with the ones previously suggested and highlight differences between them.