Evaluation of Deep Learning Techniques for Entity Matching

Abstract

: Application data inevitably has inconsistencies that may cause malfunctioning in daily operations and compromise analytical results. A particular type of inconsistency is the presence of duplicates, e.g., multiple and non-identical representations of the same information. Entity matching (EM) refers to the problem of determining whether two data instances are duplicates. Two deep learning solutions, DeepMatcher and Ditto, have recently achieved state-of-the-art results in EM. However, neither solution considered duplicates with character-level variations, which are pervasive in real-world databases. This paper presents a comparative evaluation between DeepMatcher and Ditto on datasets from a diverse array of domains with such variations and textual patterns that were previously ignored. The results showed that the two solutions experienced a considerable drop in accuracy, while Ditto was more robust than DeepMatcher.