Method and apparatus for entity alignment

A computer-implemented method for entity alignment, the method comprising the following steps:

• obtaining a first plurality of initial embeddings of entities of a first graph and a second plurality of initial embeddings of entities of a second graph based on a pre-trained model, wherein the first plurality of initial embeddings and the second plurality of initial embeddings are in a unified space;
• and
• learning entity alignment between the first graph and the second graph over the first plurality of initial embeddings and the second plurality of initial embeddings by at least one encoder to push non-aligned entities far away using a relative similarity metric, wherein negative sampling for non-aligned entities of an entity of one of the first graph or the second graph is performed on the one of the first graph or the second graph during the learning.

A computer-implemented method for entity alignment of graphs with natural languages, comprising the following steps:

• obtaining a first plurality of initial embeddings of entities of a first graph and a second plurality of initial embeddings of entities of a second graph based on a pre-trained language model, wherein the first graph and the second graph include the same or different languages, and the first plurality of initial embeddings and the second plurality of initial embeddings are in a unified space;
• and
• learning entity alignment between the first graph and the second graph over the first plurality of initial embeddings and the second plurality of initial embeddings by at least one encoder to push non-aligned entities far away using a relative similarity metric, wherein negative sampling for non-aligned entities of an entity of one of the first graph or the second graph is performed on the one of the first graph or the second graph during the learning.

A non-transitory computer readable medium on which is stored computer code for entity alignment, the computer code when executed by a processor, causing the processor to perform the following steps:

• obtaining a first plurality of initial embeddings of entities of a first graph and a second plurality of initial embeddings of entities of a second graph based on a pre-trained model, wherein the first plurality of initial embeddings and the second plurality of initial embeddings are in a unified space;
• and
• learning entity alignment between the first graph and the second graph over the first plurality of initial embeddings and the second plurality of initial embeddings by at least one encoder to push non-aligned entities far away using a relative similarity metric, wherein negative sampling for non-aligned entities of an entity of one of the first graph or the second graph is performed on the one of the first graph or the second graph during the learning.