Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
This work addresses the problem of unreliable evaluations in entity matching for researchers and practitioners, highlighting incremental improvements in benchmark design.
The paper tackles the performance gap between deep learning-based entity matching methods on standard benchmarks and real-world applications by identifying issues in benchmark construction, and it builds a new corpus and benchmarks that reveal these methods are significantly overestimated.
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