Cross-Language Entity Alignment for Joint Entity Screening and Dual Relation Graph

Abstract

In knowledge graphs of different languages, entity alignment is usuallyinterfered by problems such as structural heterogeneity and languagedifference. The emergence of these problems seriously affects the taskof entity alignment. Existing methods continuously improve alignmentstrategies, but often ignore the impact of triples with the same headand tail entity names on the entity alignment task. This paper takes thehead entity in the triple as the central entity, and proposes an entityalignment method that effectively utilizes neighbor entities with thesame entity name as the central entity. We take advantage of the multi-order properties of Graph Convolutional Neural Networks to processentities, relationships and attributes in the source and target knowl-edge graphs through knowledge structure embedding and entity nameembedding. We define the primal graph and the dual relation graphrespectively, the former is composed of two knowledge graphs together,and the latter is constructed by the association of relations in triplesto strengthen the role of relations. We use the primal graph and thedual relation graph to process the entities and relationships in knowledgegraphs. We compare the output vectors obtained by the two meth-ods, and then select the optimal output vector, combine the entityID to remove the neighbor entities that are completely equivalent tothe central entity, and finally obtain the final alignment result throughneighbor sampling and the similarity calculation of the correspondingentity. Experimental results on real datasets show that our methodsignificantly helps improve the task of cross-lingual entity alignment.

Sequence Generation with Label Augmentation for Relation Extraction

“…The first one designs novel modules or model architectures, for example, Chen, Zhang, and Huang (2022) proposes a framework of text and graph to learn relational reasoning patterns for relational triple extraction. The second one utilizes various external datasets or resources to enrich the input (Cabot and Navigli 2021;Zhang et al 2022a;Paolini et al 2021;Lu et al 2022). The last one aims to design proper generation schema.…”