Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

TL;DR

This paper identifies biases in existing entity matching benchmarks caused by unrealistic assumptions and introduces a new, more representative benchmark dataset to better evaluate EM methods in real-world scenarios.

Contribution

It re-constructs EM benchmarks by incorporating open entities, imbalanced labels, and multimodal data, providing a more realistic evaluation environment.

Findings

Previous benchmarks overestimate EM performance due to biased assumptions.

New benchmarks reveal greater challenges in real-world entity matching.

Experimental results show current methods perform less effectively in realistic settings.

Abstract

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark construction process, which is inconsistent with the nature of entity matching and therefore leads to biased evaluations of current EM approaches. To this end, we build a new EM corpus and re-construct EM benchmarks to challenge critical assumptions implicit in the previous benchmark construction process by step-wisely changing the restricted entities, balanced labels, and single-modal records in previous benchmarks into open entities, imbalanced labels, and multimodal records in an open environment. Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching. The constructed benchmarks and code are publicly released