  Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like
(h, r, ?) in different languages by reasoning a tail entity t thus improving
multilingual knowledge graphs. Previous studies leverage multilingual
pretrained language models (PLMs) and the generative paradigm to achieve mKGC.
Although multilingual pretrained language models contain extensive knowledge of
different languages, its pretraining tasks cannot be directly aligned with the
mKGC tasks. Moreover, the majority of KGs and PLMs currently available exhibit
a pronounced English-centric bias. This makes it difficult for mKGC to achieve
good results, particularly in the context of low-resource languages. To
overcome previous problems, this paper introduces global and local knowledge
constraints for mKGC. The former is used to constrain the reasoning of answer
entities, while the latter is used to enhance the representation of query
contexts. The proposed method makes the pretrained model better adapt to the
mKGC task. Experimental results on public datasets demonstrate that our method
outperforms the previous SOTA on Hits@1 and Hits@10 by an average of 12.32% and
16.03%, which indicates that our proposed method has significant enhancement on
mKGC.
