Reuse and Adaptation for Entity Resolution through Transfer Learning
arXiv:1809.11084
Abstract
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.
References in corpus (5)
Cited by in corpus (8)
- Neural Networks for Entity Matching: A Survey
- End-to-End Entity Resolution for Big Data: A Survey
- Low-resource Deep Entity Resolution with Transfer and Active Learning
- A Survey of Blocking and Filtering Techniques for Entity Resolution
- RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
- Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
- Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
- Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation