Using descriptions for explaining entity matches

Finding entity matches in large datasets is currently one of the most attractive research challenges. The recent interest of the research community towards Machine and Deep Learning techniques has led to the development of many and reliable approaches. Nevertheless, these are conceived as black-box tools that identify the matches between the entities provided as input. The lack of explainability of the process hampers its application to real-world scenarios where domain experts need to know and understand the reasons why entities can be considered as match, i.e., they represent the same real-world entity. In this paper, we show how data descriptions—a set of compact, readable and insightful formulas of boolean predicates—can be used to guide domain experts in understanding and evaluating the results of entity matching processes.