# AutoBlock: A Hands-off Blocking Framework for Entity Matching

arXiv: https://arxiv.org/abs/1912.03417 · HTML: https://mlsanity.com/paper/1912.03417/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2304.12329/) (2023)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins](https://mlsanity.com/paper/2106.01501/) (2021)
- [SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines](https://mlsanity.com/paper/2303.03132/) (2023)
- [Evaluating Blocking Biases in Entity Matching](https://mlsanity.com/paper/2409.16410/) (2024)
- [Towards Universal Dense Blocking for Entity Resolution](https://mlsanity.com/paper/2404.14831/) (2024)
- [TransClean: Finding False Positives in Multi-Source Entity Matching under Real-World Conditions via Transitive Consistency](https://mlsanity.com/paper/2506.04006/) (2025)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)
- [ShallowBlocker: Improving Set Similarity Joins for Blocking](https://mlsanity.com/paper/2312.15835/) (2023)
- [A Tight Expressivity Hierarchy for GNN-Based Entity Resolution in Master Data Management](https://mlsanity.com/paper/2603.27154/) (2026)

## Abstract (arXiv)

Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity. Virtually every entity matching task on large datasets requires blocking, a step that reduces the number of record pairs to be matched. However, most of the traditional blocking methods are learning-free and key-based, and their successes are largely built on laborious human effort in cleaning data and designing blocking keys. In this paper, we propose AutoBlock, a novel hands-off blocking framework for entity matching, based on similarity-preserving representation learning and nearest neighbor search. Our contributions include: (a) Automation: AutoBlock frees users from laborious data cleaning and blocking key tuning. (b) Scalability: AutoBlock has a sub-quadratic total time complexity and can be easily deployed for millions of records. (c) Effectiveness: AutoBlock outperforms a wide range of competitive baselines on multiple large-scale, real-world datasets, especially when datasets are dirty and/or unstructured.