# Ground Truth Inference for Weakly Supervised Entity Matching arXiv: https://arxiv.org/abs/2211.06975 ยท HTML: https://mlsanity.com/paper/2211.06975/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 85 references that are in the map. - First posted on arXiv 13 Nov 2022. ## Builds on (most cited first, 28 of 28) - [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016) - [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019) - [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017) - [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016) - [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020) - [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018) - [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020) - [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021) - [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014) - [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017) - [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020) - [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018) - [End-to-End Weak Supervision](https://mlsanity.com/paper/2107.02233/) (2021) - [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019) - [GOGGLES: Automatic Image Labeling with Affinity Coding](https://mlsanity.com/paper/1903.04552/) (2019) - [Learning to Solve Hard Minimal Problems](https://mlsanity.com/paper/2112.03424/) (2021) - [Strength from Weakness: Fast Learning Using Weak Supervision](https://mlsanity.com/paper/2002.08483/) (2020) - [Incremental Knowledge Base Construction Using DeepDive](https://mlsanity.com/paper/1502.00731/) (2015) - [Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization](https://mlsanity.com/paper/2009.13586/) (2020) - [Learning Hyper Label Model for Programmatic Weak Supervision](https://mlsanity.com/paper/2207.13545/) (2022) - [Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples](https://mlsanity.com/paper/2103.04489/) (2021) - [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021) - [Fonduer: Knowledge Base Construction from Richly Formatted Data](https://mlsanity.com/paper/1703.05028/) (2017) - [Alaska: A Flexible Benchmark for Data Integration Tasks](https://mlsanity.com/paper/2101.11259/) (2021) - [Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2106.07193/) (2021) - [Demonstration of Panda: A Weakly Supervised Entity Matching System](https://mlsanity.com/paper/2106.10821/) (2021) - [Learning to be a Statistician: Learned Estimator for Number of Distinct Values](https://mlsanity.com/paper/2202.02800/) (2022) - [Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs](https://mlsanity.com/paper/2104.12589/) (2021) ## Built on by (most cited first, 5 of 5) - [Data Collection and Labeling Techniques for Machine Learning](https://mlsanity.com/paper/2407.12793/) (2024) - [Leveraging Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.20624/) (2024) - [RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision](https://mlsanity.com/paper/2307.04870/) (2023) - [Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution](https://mlsanity.com/paper/2605.25814/) (2026) - [Beyond Scale and Generation: Understanding Language Model-based Entity Matching](https://mlsanity.com/paper/2607.24688/) (2026) ## Abstract (arXiv) Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine learning (ML) models currently achieve state-of-the-art matching performance; however, they require many labeled examples, which are often expensive or infeasible to obtain. This has inspired us to approach data labeling for EM using weak supervision. In particular, we use the labeling function abstraction popularized by Snorkel, where each labeling function (LF) is a user-provided program that can generate many noisy match/non-match labels quickly and cheaply. Given a set of user-written LFs, the quality of data labeling depends on a labeling model to accurately infer the ground-truth labels. In this work, we first propose a simple but powerful labeling model for general weak supervision tasks. Then, we tailor the labeling model specifically to the task of entity matching by considering the EM-specific transitivity property. The general form of our labeling model is simple while substantially outperforming the best existing method across ten general weak supervision datasets. To tailor the labeling model for EM, we formulate an approach to ensure that the final predictions of the labeling model satisfy the transitivity property required in EM, utilizing an exact solution where possible and an ML-based approximation in remaining cases. On two single-table and nine two-table real-world EM datasets, we show that our labeling model results in a 9% higher F1 score on average than the best existing method. We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.