  Across various domains, data from different sources such as Baidu Baike and
Wikipedia often manifest in distinct forms. Current entity matching
methodologies predominantly focus on homogeneous data, characterized by
attributes that share the same structure and concise attribute values. However,
this orientation poses challenges in handling data with diverse formats.
Moreover, prevailing approaches aggregate the similarity of attribute values
between corresponding attributes to ascertain entity similarity. Yet, they
often overlook the intricate interrelationships between attributes, where one
attribute may have multiple associations. The simplistic approach of pairwise
attribute comparison fails to harness the wealth of information encapsulated
within entities.To address these challenges, we introduce a novel entity
matching model, dubbed Entity Matching Model for Capturing Complex Attribute
Relationships(EMM-CCAR),built upon pre-trained models. Specifically, this model
transforms the matching task into a sequence matching problem to mitigate the
impact of varying data formats. Moreover, by introducing attention mechanisms,
it identifies complex relationships between attributes, emphasizing the degree
of matching among multiple attributes rather than one-to-one correspondences.
Through the integration of the EMM-CCAR model, we adeptly surmount the
challenges posed by data heterogeneity and intricate attribute
interdependencies. In comparison with the prevalent DER-SSM and Ditto
approaches, our model achieves improvements of approximately 4% and 1% in F1
scores, respectively. This furnishes a robust solution for addressing the
intricacies of attribute complexity in entity matching.
