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Entity matching (EM) is the most critical step for entity resolution (ER).While current deep learningbased methods achieve very impressive performance onstandard EM benchmarks, their realworld application performance is muchfrustrating. In this paper, we highlight that such the gap between reality andideality stems from the unreasonable benchmark construction process, which isinconsistent with the nature of entity matching and therefore leads to biasedevaluations of current EM approaches. To this end, we build a new EM corpus andre-construct EM benchmarks to challenge critical assumptions implicit in theprevious benchmark construction process by step-wisely changing the restrictedentities, balanced labels, and single-modal records in previous benchmarks intoopen entities, imbalanced labels, and multimodal records in an openenvironment. Experimental results demonstrate that the assumptions made in theprevious benchmark construction process are not coincidental with the openenvironment, which conceal the main challenges of the task and thereforesignificantly overestimate the current progress of entity matching. Theconstructed benchmarks and code are publicly released

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