  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.
