| Summary: | Graph convolutional network-based methods have become mainstream for cross-language entity alignment. The graph convolutional network has multi-order characteristics that not only process data more conveniently but also reduce the interference of noise effectively. Although the existing methods have achieved good results for the task of cross-language entity alignment, they have often overlooked the same entity names in the real corpus, resulting in an entity-matching result that was not ideal. Therefore, this study proposed a neighboring-entity-screening rule by combining the entity name and the attribute (NENA) to reduce the influence of these issues. We used the NENA-screening rule to filter and delete redundant equivalent entities and to construct a dual-relation graph as auxiliary evidence for scenarios when the attribute information may be insufficient.This study adopted a graph convolutional network in order to embed knowledge graphs and entity names into a unified vector space, and then a down-sampling method was used to extract the neighboring entities of each entity, thus forming sub-graphs of the two knowledge graphs. We embedded the sub-graphs into the GCN, as the new input, and then we used a cross-graph-matching module to finally achieve alignment. Our results on the DBP15K dataset showed that our approach significantly improved the overall entity alignment.On the sub-dataset ZH-EN of DBP15K, the value of Hits@1 improved by 1.38%, as compared to the best approach mentioned in this paper, and it was useful for the construction and completion of the open knowledge graph.
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