# A Survey of Blocking and Filtering Techniques for Entity Resolution

arXiv: https://arxiv.org/abs/1905.06167 · HTML: https://mlsanity.com/paper/1905.06167/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 8 of 8)

- [Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching](https://mlsanity.com/paper/2307.02726/) (2023)
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- [Linking Representations with Multimodal Contrastive Learning](https://mlsanity.com/paper/2304.03464/) (2023)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)
- [ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution](https://mlsanity.com/paper/2506.13792/) (2025)
- [Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generation](https://mlsanity.com/paper/2510.14271/) (2025)
- [OpenSanctions Pairs: Large-Scale Entity Matching with LLMs](https://mlsanity.com/paper/2603.11051/) (2026)

## Abstract (arXiv)

Efficiency techniques are an integral part of Entity Resolution, since its infancy. In this survey, we organized the bulk of works in the field into Blocking, Filtering and hybrid techniques, facilitating their understanding and use. We also provided an in-dept coverage of each category, further classifying the corresponding works into novel sub-categories. Lately, the efficiency techniques have received more attention, due to the rise of Big Data. This includes large volumes of semi-structured data, which pose challenges not only to the scalability of efficiency techniques, but also to their core assumptions: the requirement of Blocking for schema knowledge and of Filtering for high similarity thresholds. The former led to the introduction of schema-agnostic Blocking in conjunction with Block Processing techniques, while the latter led to more relaxed criteria of similarity. Our survey covers these new fields in detail, putting in context all relevant works.