PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

TL;DR

This paper introduces PromptEM, a novel prompt-tuning approach for low-resource generalized entity matching, effectively reducing labeling efforts and improving performance across diverse data formats.

Contribution

PromptEM is the first method to apply prompt-tuning to low-resource GEM, addressing prompt design, pseudo-label quality, and efficient self-training.

Findings

Outperforms existing methods on eight benchmarks.

Effective in low-resource settings with limited labeled data.

Demonstrates superior efficiency and accuracy.

Abstract

Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.