Entity Matching (EM) is a significant task involving the determination of the logical relationship between two entities, such as Same, Different, and Ambiguous. Traditional approaches to entity matching (EM) heavily depend on supervised learning, which necessitates a vast collection of high-quality labeled data. This labeling process is both time-consuming and costly, limiting the practical application of these methods. Consequently, there is a pressing demand for low-resource EM solutions that can perform effectively with minimal labeled data.