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.

本文研究了在具有限或无训练数据的情况下，通过重用和调整同一领域或相关领域数据集D_S的训练数据，是否可以在数据集D_T中训练良好的机器学习分类器，并提出了用于处理此类情况的五种算法。在五个不同领域的12个数据集上进行了全面的实验，结果表明，该算法提供了显著的好处，如在固定训练数据大小的情况下提供卓越的性能。

通过转移学习进行实体匹配的重用和适应