Reuse and Adaptation for Entity Resolution through Transfer Learning

TL;DR

This paper explores transfer learning for entity resolution, enabling classifiers trained on one dataset to be adapted for use on related datasets with limited or no training data, reducing manual effort.

Contribution

It introduces a distributed representation approach and five algorithms for effective reuse and adaptation of training data across related datasets in entity resolution.

Findings

Algorithms outperform baseline methods in diverse datasets

Significant performance improvements with limited training data

Effective transfer learning reduces manual feature engineering

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.