# Auto-Tag: Tagging-Data-By-Example in Data Lakes

arXiv: https://arxiv.org/abs/2112.06049 · HTML: https://mlsanity.com/paper/2112.06049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes](https://mlsanity.com/paper/2104.04659/) (2021)
- [FlashProfile: A Framework for Synthesizing Data Profiles](https://mlsanity.com/paper/1709.05725/) (2017)
- [Extracting Syntactic Patterns from Databases](https://mlsanity.com/paper/1710.11528/) (2017)
- [Data Validation](https://mlsanity.com/paper/2012.12028/) (2020)
- [Harvesting Entities from the Web Using Unique Identifiers -- IBEX](https://mlsanity.com/paper/1505.00841/) (2015)

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

- [Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models](https://mlsanity.com/paper/2508.09403/) (2025)
- [AdaTyper: Adaptive Semantic Column Type Detection](https://mlsanity.com/paper/2311.13806/) (2023)
- [Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables](https://mlsanity.com/paper/2504.10762/) (2025)
- [TACO: Task-Aware Column Description Generation Using LLMs](https://mlsanity.com/paper/2606.21685/) (2026)

## Abstract (arXiv)

As data lakes become increasingly popular in large enterprises today, there is a growing need to tag or classify data assets (e.g., files and databases) in data lakes with additional metadata (e.g., semantic column-types), as the inferred metadata can enable a range of downstream applications like data governance (e.g., GDPR compliance), and dataset search. Given the sheer size of today's enterprise data lakes with petabytes of data and millions of data assets, it is imperative that data assets can be ``auto-tagged'', using lightweight inference algorithms and minimal user input. In this work, we develop Auto-Tag, a corpus-driven approach that automates data-tagging of textitcustom data types in enterprise data lakes. Using Auto-Tag, users only need to provide textitone example column to demonstrate the desired data-type to tag. Leveraging an index structure built offline using a lightweight scan of the data lake, which is analogous to pre-training in machine learning, Auto-Tag can infer suitable data patterns to best ``describe'' the underlying ``domain'' of the given column at an interactive speed, which can then be used to tag additional data of the same ``type'' in data lakes. The Auto-Tag approach can adapt to custom data-types, and is shown to be both accurate and efficient. Part of Auto-Tag ships as a ``custom-classification'' feature in a cloud-based data governance and catalog solution textitAzure Purview.