mlsanityData to 7 Oct 2026

(Almost) All of Entity Resolution

arXiv 2008.04443 ↗First posted 10 Aug 2020stat.ME cs.DB stat.MLcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Aug 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 6; 2024: 4; 2025: 3; 2026: 4.

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  1. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 55
  2. 2024Match, Compare, or Select? An Investigation of Large Language Models for Entity Matchingcited by 32
  3. 2022A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literalscited by 4
  4. 2023A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithmscited by 4
  5. 2023LinkTransformer: A Unified Package for Record Linkage with Transformer Language Modelscited by 4
  6. 2023Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketchingcited by 4
  7. 2024Deep Learning for Economistscited by 4
  8. 2022Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.orgcited by 3
  9. 2025Empowering Tabular Data Preparation with Language Models: Why and How?cited by 3
  10. 2022Why the Rich Get Richer? On the Balancedness of Random Partition Modelscited by 2
  11. 2023Quantifying Character Similarity with Vision Transformerscited by 2
  12. 2021Multifile Partitioning for Record Linkage and Duplicate Detectioncited by 1
11 more
  1. 2021Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Groupcited by 1
  2. 2023PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguationcited by 1
  3. 2023A Primer on the Data Cleaning Pipelinecited by 1
  4. 2024How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguationcited by 1
  5. 2021Big Data is not the New Oil: Common Misconceptions about Population Datacited by 0
  6. 2025Identity resolution of software metadata using Large Language Modelscited by 0
  7. 2025A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolutioncited by 0
  8. 2026Household Movement Detection in Mixed-Format Occupancy Data Using LLM-Based Entity Resolutioncited by 0
  9. 2026Entity Resolution in Practice: Lessons from a Self-Serve Pipelinecited by 0
  10. 2026When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotatorscited by 0
  11. 2026Billiger.de Products: A Bilingual Entity Matching Benchmarkcited by 0