Ground Truth Inference for Weakly Supervised Entity Matching

• URL: http://arxiv.org/abs/2211.06975v1
• Date: Sun, 13 Nov 2022 17:57:07 GMT
• Title: Ground Truth Inference for Weakly Supervised Entity Matching
• Authors: Renzhi Wu, Alexander Bendeck, Xu Chu, Yeye He
• Abstract summary: We propose a simple but powerful labeling model for weak supervision tasks. We then tailor the labeling model specifically to the task of entity matching. We show that our labeling model results in a 9% higher F1 score on average than the best existing method.
• Score: 76.6732856489872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine learning (ML) models currently achieve state-of-the-art matching performance; however, they require many labeled examples, which are often expensive or infeasible to obtain. This has inspired us to approach data labeling for EM using weak supervision. In particular, we use the labeling function abstraction popularized by Snorkel, where each labeling function (LF) is a user-provided program that can generate many noisy match/non-match labels quickly and cheaply. Given a set of user-written LFs, the quality of data labeling depends on a labeling model to accurately infer the ground-truth labels. In this work, we first propose a simple but powerful labeling model for general weak supervision tasks. Then, we tailor the labeling model specifically to the task of entity matching by considering the EM-specific transitivity property. The general form of our labeling model is simple while substantially outperforming the best existing method across ten general weak supervision datasets. To tailor the labeling model for EM, we formulate an approach to ensure that the final predictions of the labeling model satisfy the transitivity property required in EM, utilizing an exact solution where possible and an ML-based approximation in remaining cases. On two single-table and nine two-table real-world EM datasets, we show that our labeling model results in a 9% higher F1 score on average than the best existing method. We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.

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