TAILOR: a record linkage toolbox

TLDR

Data cleaning ensures database quality, and record linkage—identifying duplicate records—is a key component used across knowledge discovery, data warehousing, system integration, and e‑services. The paper aims to solve record linkage by applying machine‑learning techniques. We propose three machine‑learning models and an extensible toolbox, TAILOR, that lets users build, tune, and integrate models with existing tools. Experimental evaluation on synthetic and real data shows that the proposed models outperform existing methods in accuracy and performance, leading to the development of the interactive toolbox TAILOR.

Abstract

Data cleaning is a vital process that ensures the quality of data stored in real-world databases. Data cleaning problems are frequently encountered in many research areas, such as knowledge discovery in databases, data warehousing, system integration and e-services. The process of identifying the record pairs that represent the same entity (duplicate records), commonly known as record linkage, is one of the essential elements of data cleaning. In this paper, we address the record linkage problem by adopting a machine learning approach. Three models are proposed and are analyzed empirically. Since no existing model, including those proposed in this paper, has been proved to be superior, we have developed an interactive record linkage toolbox named TAILOR (backwards acronym for "RecOrd LInkAge Toolbox"). Users of TAILOR can build their own record linkage models by tuning system parameters and by plugging in in-house-developed and public-domain tools. The proposed toolbox serves as a framework for the record linkage process, and is designed in an extensible way to interface with existing and future record linkage models. We have conducted an extensive experimental study to evaluate our proposed models using not only synthetic but also real data. The results show that the proposed machine-learning record linkage models outperform the existing ones both in accuracy and in performance.