Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs, such as alignments between multilingual KGs with common entities and relations, a deeper understanding of the ways in which multilingual KG completion (MKGC) can aid the creation of multilingual KG alignments (MKGA) is still limited. Motivated by the observation that structural inconsistencies -- the main challenge for MKGA models -- can be mitigated through KG completion methods, we propose a novel model for jointly completing and aligning knowledge graphs. The proposed model combines two components that jointly accomplish KG completion and alignment. These two components employ relation-aware graph neural networks that we propose to encode multi-hop neighborhood structures into entity and relation representations. Moreover, we also propose (i) a structural inconsistency reduction mechanism to incorporate information from the completion into the alignment component, and (ii) an alignment seed enlargement and triple transferring mechanism to enlarge alignment seeds and transfer triples during KGs alignment. Extensive experiments on a public multilingual benchmark show that our proposed model outperforms existing competitive baselines, obtaining new state-of-the-art results on both MKGC and MKGA tasks. We publicly release the implementation of our model at https://github.com/vinhsuhi/JMAC
翻译:知识图(KG)的调整和完成通常被视为两个独立的任务。虽然最近的工作利用了多知识图(KG)与共同实体和关系之间的组合等多种知识图(KG)的实体和关系,例如多语言知识图(KG)与共同实体和关系之间的组合,但对于多语言KG的完成(MKGC)如何帮助创建多语言知识图(MKGG)的调整和完成(MKGGA)调整和完成(MKGGA)模式的主要挑战 -- -- 结构性不一致通常被视为两个独立的任务。虽然最近的工作利用了多个知识图(实体和关系关系)的实体之间的组合和组合,加深了对多语言KGGG(K)的组合和关系调整,但是,由于以下观察的动力,我们提出了一种结构上的不一致性减少机制,将完成的完成后的信息纳入到协调部分,以及(二)调整种子的扩大和三重传输机制,以扩大协调种子和调整知识图的组合。关于公共多语言基准的实验显示我们提议的模型超越了现有的竞争性基线,在MGAK公司/MGA执行新状态。