  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

Computation and Language