WDC Products: A Multi-Dimensional Entity Matching Benchmark

TL;DR

WDC Products is a comprehensive benchmark for entity matching that evaluates systems across multiple real-world dimensions, including unseen entities and corner-cases, using both pairwise and multi-class formulations.

Contribution

It introduces a novel multi-dimensional benchmark for entity matching that considers unseen entities, corner-cases, and varying training set sizes, with both pairwise and multi-class tasks.

Findings

All systems struggle with unseen entities.

Contrastive learning is more data-efficient than cross-encoders.

Benchmark enables systematic evaluation across multiple challenging dimensions.

Abstract

The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the space along such dimensions or they provide for the evaluation of matching methods along a single dimension, for instance the amount of training data. This paper presents WDC Products, an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-world data. The three dimensions are (i) amount of corner-cases (ii) generalization to unseen entities, and (iii) development set size (training set plus validation set). Generalization to unseen entities is a dimension not covered by any of the existing English-language benchmarks yet but is crucial for evaluating the robustness of entity matching systems. Instead of learning how to match entity pairs, entity matching can also be formulated as a multi-class classification task that requires the matcher to recognize individual entities. WDC Products is the first benchmark that provides a pair-wise and a multi-class formulation of the same tasks. We evaluate WDC Products using several state-of-the-art matching systems, including Ditto, HierGAT, and R-SupCon. The evaluation shows that all matching systems struggle with unseen entities to varying degrees. It also shows that for entity matching contrastive learning is more training data efficient compared to cross-encoders.