# MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching

arXiv: https://arxiv.org/abs/2308.01927 · HTML: https://mlsanity.com/paper/2308.01927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016)
- [Low-resource Deep Entity Resolution with Transfer and Active Learning](https://mlsanity.com/paper/1906.08042/) (2019)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities](https://mlsanity.com/paper/2205.10312/) (2022)
- [Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation](https://mlsanity.com/paper/2207.04122/) (2022)
- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [Machamp: A Generalized Entity Matching Benchmark](https://mlsanity.com/paper/2106.08455/) (2021)
- [Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples](https://mlsanity.com/paper/2103.04489/) (2021)
- [EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs](https://mlsanity.com/paper/2006.06890/) (2020)
- [PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching](https://mlsanity.com/paper/2207.04802/) (2022)
- [Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation](https://mlsanity.com/paper/2110.14509/) (2021)
- [Active Deep Learning on Entity Resolution by Risk Sampling](https://mlsanity.com/paper/2012.12960/) (2020)
- [FlexER: Flexible Entity Resolution for Multiple Intents](https://mlsanity.com/paper/2209.07569/) (2022)
- [Pangolin: An Efficient and Flexible Graph Pattern Mining System on CPU and GPU](https://mlsanity.com/paper/1911.06969/) (2019)
- [Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity](https://mlsanity.com/paper/2208.13376/) (2022)

## Built on by (most cited first, 2 of 2)

- [Aligning Multiple Knowledge Graphs in a Single Pass](https://mlsanity.com/paper/2408.00662/) (2024)
- [Unlocking the Power of Large Language Models for Multi-table Entity Matching](https://mlsanity.com/paper/2604.21238/) (2026)

## Abstract (arXiv)

Entity Matching (EM), which aims to identify all entity pairs referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data management systems. Due to the labeling process of EM being extremely labor-intensive, unsupervised EM is more applicable than supervised EM in practical scenarios. Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM). Unfortunately, effective and efficient unsupervised multi-table EM remains under-explored. To fill this gap, this paper formally studies the problem of unsupervised multi-table entity matching and proposes an effective and efficient solution, termed as MultiEM. MultiEM is a parallelable pipeline of enhanced entity representation, table-wise hierarchical merging, and density-based pruning. Extensive experimental results on six real-world benchmark datasets demonstrate the superiority of MultiEM in terms of effectiveness and efficiency.