A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms
This work addresses a critical gap for researchers in entity resolution by exposing flaws in current evaluation practices and providing improved benchmarks, though it is incremental as it focuses on dataset quality rather than algorithmic innovation.
The paper tackled the problem that existing benchmark datasets for learning-based entity resolution are too easy, showing that most popular datasets pose simple classification tasks and are unsuitable for proper evaluation. They proposed a new methodology to create more challenging benchmarks, generating four new matching tasks verified to be more difficult and appropriate for advancing the field.
Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tackle ER challenges over the years, with recent emphasis placed on machine and deep learning methods for the matching phase. However, the quality of the benchmark datasets typically used in the experimental evaluations of learning-based matching algorithms has not been examined in the literature. To cover this gap, we propose four different approaches to assessing the difficulty and appropriateness of 13 established datasets: two theoretical approaches, which involve new measures of linearity and existing measures of complexity, and two practical approaches: the difference between the best non-linear and linear matchers, as well as the difference between the best learning-based matcher and the perfect oracle. Our analysis demonstrates that most of the popular datasets pose rather easy classification tasks. As a result, they are not suitable for properly evaluating learning-based matching algorithms. To address this issue, we propose a new methodology for yielding benchmark datasets. We put it into practice by creating four new matching tasks, and we verify that these new benchmarks are more challenging and therefore more suitable for further advancements in the field.