# Graph-based Active Learning for Entity Cluster Repair

arXiv: https://arxiv.org/abs/2401.14992 · HTML: https://mlsanity.com/paper/2401.14992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Using ChatGPT for Entity Matching](https://mlsanity.com/paper/2305.03423/) (2023)
- [How Robust Are Graph Neural Networks to Structural Noise?](https://mlsanity.com/paper/1912.10206/) (2019)
- [FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models](https://mlsanity.com/paper/2311.15614/) (2023)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Combating Bilateral Edge Noise for Robust Link Prediction](https://mlsanity.com/paper/2311.01196/) (2023)
- [A Comparative Study on Robust Graph Neural Networks to Structural Noises](https://mlsanity.com/paper/2112.06070/) (2021)
- [Distributed Holistic Clustering on Linked Data](https://mlsanity.com/paper/1708.09299/) (2017)

## Built on by (most cited first, 1 of 1)

- [Entity Resolution in Practice: Lessons from a Self-Serve Pipeline](https://mlsanity.com/paper/2607.26298/) (2026)

## Abstract (arXiv)

Cluster repair methods aim to determine errors in clusters and modify them so that each cluster consists of records representing the same entity. Current cluster repair methodologies primarily assume duplicate-free data sources, where each record from one source corresponds to a unique record from another. However, real-world data often deviates from this assumption due to quality issues. Recent approaches apply clustering methods in combination with link categorization methods so they can be applied to data sources with duplicates. Nevertheless, the results do not show a clear picture since the quality highly varies depending on the configuration and dataset. In this study, we introduce a novel approach for cluster repair that utilizes graph metrics derived from the underlying similarity graphs. These metrics are pivotal in constructing a classification model to distinguish between correct and incorrect edges. To address the challenge of limited training data, we integrate an active learning mechanism tailored to cluster-specific attributes. The evaluation shows that the method outperforms existing cluster repair methods without distinguishing between duplicate-free or dirty data sources. Notably, our modified active learning strategy exhibits enhanced performance when dealing with datasets containing duplicates, showcasing its effectiveness in such scenarios.