Tailoring Entity Matching for Industrial Settings

Abstract

Entity matching has received significant attention from the research community over many years. Despite some limited success, most state-of-the-art methods see no widespread usage in industry.

In this paper, we present the author's PhD research, which aims at identifying issues that hold techniques and methods developed by the research community back from use in industry, and look at how they might be adapted to address those issues. In our proposed approach, we implement a modular framework, which will be used for real-world user testing and quantitative experiments of our adapted methods. We will have three main contributions from our research: 1) We develop a modular framework for interactive entity matching combining intra- and inter-session iterations. 2) We show how active learning methods for entity matching can be adapted to learn not only classification of matches but also classification of which records are of interest to the user jointly, and how it compares to current methods. 3) We show how deep learning can be used to synthesize interpretable rules for entity matching, and how it compares to traditional methods