  Knowledge Graph Completion (KGC) predicts missing facts in an incomplete
Knowledge Graph. Almost all of existing KGC research is applicable to only one
KG at a time, and in one language only. However, different language speakers
may maintain separate KGs in their language and no individual KG is expected to
be complete. Moreover, common entities or relations in these KGs have different
surface forms and IDs, leading to ID proliferation. Entity alignment (EA) and
relation alignment (RA) tasks resolve this by recognizing pairs of entity
(relation) IDs in different KGs that represent the same entity (relation). This
can further help prediction of missing facts, since knowledge from one KG is
likely to benefit completion of another. High confidence predictions may also
add valuable information for the alignment tasks. In response, we study the
novel task of jointly training multilingual KGC, relation alignment and entity
alignment models. We present ALIGNKGC, which uses some seed alignments to
jointly optimize all three of KGC, EA and RA losses. A key component of
ALIGNKGC is an embedding based soft notion of asymmetric overlap defined on the
(subject, object) set signatures of relations this aids in better predicting
relations that are equivalent to or implied by other relations. Extensive
experiments with DBPedia in five languages establish the benefits of joint
training for all tasks, achieving 10-32 MRR improvements of ALIGNKGC over a
strong state-of-the-art single-KGC system completion model over each
monolingual KG . Further, ALIGNKGC achieves reasonable gains in EA and RA tasks
over a vanilla completion model over a KG that combines all facts without
alignment, underscoring the value of joint training for these tasks.
