# (Almost) All of Entity Resolution

arXiv: https://arxiv.org/abs/2008.04443 · HTML: https://mlsanity.com/paper/2008.04443/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [KG-RAG: Bridging the Gap Between Knowledge and Creativity](https://mlsanity.com/paper/2405.12035/) (2024)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals](https://mlsanity.com/paper/2201.09555/) (2022)
- [A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms](https://mlsanity.com/paper/2307.01231/) (2023)
- [LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models](https://mlsanity.com/paper/2309.00789/) (2023)
- [Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching](https://mlsanity.com/paper/2312.11801/) (2023)
- [Deep Learning for Economists](https://mlsanity.com/paper/2407.15339/) (2024)
- [Empowering Tabular Data Preparation with Language Models: Why and How?](https://mlsanity.com/paper/2508.01556/) (2025)
- [Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org](https://mlsanity.com/paper/2210.01230/) (2022)
- [Why the Rich Get Richer? On the Balancedness of Random Partition Models](https://mlsanity.com/paper/2201.12697/) (2022)
- [Quantifying Character Similarity with Vision Transformers](https://mlsanity.com/paper/2305.14672/) (2023)
- [Multifile Partitioning for Record Linkage and Duplicate Detection](https://mlsanity.com/paper/2110.03839/) (2021)
- [Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group](https://mlsanity.com/paper/2111.01767/) (2021)
- [PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation](https://mlsanity.com/paper/2301.03591/) (2023)
- [A Primer on the Data Cleaning Pipeline](https://mlsanity.com/paper/2307.13219/) (2023)
- [How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation](https://mlsanity.com/paper/2404.05622/) (2024)
- [Big Data is not the New Oil: Common Misconceptions about Population Data](https://mlsanity.com/paper/2112.10912/) (2021)
- [Identity resolution of software metadata using Large Language Models](https://mlsanity.com/paper/2505.23500/) (2025)
- [A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolution](https://mlsanity.com/paper/2508.03767/) (2025)
- [Household Movement Detection in Mixed-Format Occupancy Data Using LLM-Based Entity Resolution](https://mlsanity.com/paper/2607.21614/) (2026)
- [Entity Resolution in Practice: Lessons from a Self-Serve Pipeline](https://mlsanity.com/paper/2607.26298/) (2026)
- [When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotators](https://mlsanity.com/paper/2608.18158/) (2026)
- [Billiger.de Products: A Bilingual Entity Matching Benchmark](https://mlsanity.com/paper/2609.37713/) (2026)

## Abstract (arXiv)

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme - integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as record linkage, de-duplication, or entity resolution. In this article, we review motivational applications and seminal papers that have led to the growth of this area. Specifically, we review the foundational work that began in the 1940's and 50's that have led to modern probabilistic record linkage. We review clustering approaches to entity resolution, semi- and fully supervised methods, and canonicalization, which are being used throughout industry and academia in applications such as human rights, official statistics, medicine, citation networks, among others. Finally, we discuss current research topics of practical importance.