Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

Understand

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity.

• This decoding method may not only cause the "many-to-one" problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions.
• In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm.
• Specifically, the Easy-to-Hard strategy first retrieves the model-confident alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments.