Entity resolution (ER), precisely identifying different representations of the same real-world entities, is critical for data integration. The ER question has been studied many years, and methods have proposed to solve it. Although deep learning achieved good performance in tasks, there are some challenges regarding manual labeling model transfer. This paper proposes a novel model, Transformer-...

This paper introduces the concept of asserted resolution as a technique for entity resolution. In asserted resolution trusted information sources are used to force the equivalence (or non-equivalence) of entity references and identity structures regardless of matching conditions. The paper proposes five specific forms of assertion to support entity identity information management, the process o...

Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of entity resolution over probabilistic data (ERPD), which arises in many applications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensit...