# Reuse and Adaptation for Entity Resolution through Transfer Learning

arXiv: https://arxiv.org/abs/1809.11084 · HTML: https://mlsanity.com/paper/1809.11084/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction](https://mlsanity.com/paper/2205.05889/) (2022)
- [KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs](https://mlsanity.com/paper/2410.12480/) (2024)
- [Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation](https://mlsanity.com/paper/2110.14509/) (2021)
- [The Battleship Approach to the Low Resource Entity Matching Problem](https://mlsanity.com/paper/2311.15685/) (2023)
- [Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making](https://mlsanity.com/paper/2106.04174/) (2021)
- [Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks](https://mlsanity.com/paper/2309.11046/) (2023)
- [Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective](https://mlsanity.com/paper/2503.11963/) (2025)

## Abstract (arXiv)

Entity resolution (ER) is one of the fundamental problems in data integration, where machine learning (ML) based classifiers often provide the state-of-the-art results. Considerable human effort goes into feature engineering and training data creation. In this paper, we investigate a new problem: Given a dataset D_T for ER with limited or no training data, is it possible to train a good ML classifier on D_T by reusing and adapting the training data of dataset D_S from same or related domain? Our major contributions include (1) a distributed representation based approach to encode each tuple from diverse datasets into a standard feature space; (2) identification of common scenarios where the reuse of training data can be beneficial; and (3) five algorithms for handling each of the aforementioned scenarios. We have performed comprehensive experiments on 12 datasets from 5 different domains (publications, movies, songs, restaurants, and books). Our experiments show that our algorithms provide significant benefits such as providing superior performance for a fixed training data size.