Abstract: Example implementations relate to automatically identifying matching data entities. A determination request is received and for each of a first data entity and a second data entity, a sequence of demarcated attributes is generated. A similarity score is generated, using a trained classification model, between the first data entity and the second data entity that accounts for feature interactions between the first data entity and the second data entity. The trained classification model receives the demarcated attributes for each of the first data entity and the second data entity as inputs, and is trained using a dataset of annotated pairs of data entities. Weights in the trained classification model are determined based on a customizable loss function. In accordance with a determination that the similarity score is greater than a predetermined threshold, an indication that the first data entity matches the second data entity is generated.

Abstract: Example implementations relate to automatically identifying matching data entities. A determination request is received and for each of a first data entity and a second data entity, a sequence of demarcated attributes is generated. A similarity score is generated, using a trained classification model, between the first data entity and the second data entity that accounts for feature interactions between the first data entity and the second data entity. The trained classification model receives the demarcated attributes for each of the first data entity and the second data entity as inputs, and is trained using a dataset of annotated pairs of data entities. Weights in the trained classification model are determined based on a customizable loss function. In accordance with a determination that the similarity score is greater than a predetermined threshold, an indication that the first data entity matches the second data entity is generated.