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.

ALIGNKGC 是一种用于训练多语言知识图谱（Knowledge Graph）完成、实体对齐和关系对齐模型的方法，旨在优化三种损失。使用 DBPedia 数据集进行实验，并与单一 KGC 模型、不使用对齐的 KGC 模型进行比较，结果表明 ALIGNKGC 在这三种任务上的表现均得到了改善。

多语言知识图谱补全的关系和实体对齐

Multilingual Knowledge Graph Completion with Joint Relation and Entity  Alignment

Entity alignment which aims at linking entities with the same meaning from
different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing
research focused on learning embeddings of entities by utilizing structural
information of KGs for entity alignment. These methods can aggregate
information from neighboring nodes but may also bring noise from neighbors.
Most recently, several researchers attempted to compare neighboring nodes in
pairs to enhance the entity alignment. However, they ignored the relations
between entities which are also important for neighborhood matching. In
addition, existing methods paid less attention to the positive interactions
between the entity alignment and the relation alignment. To deal with these
issues, we propose a novel Relation-aware Neighborhood Matching model named RNM
for entity alignment. Specifically, we propose to utilize the neighborhood
matching to enhance the entity alignment. Besides comparing neighbor nodes when
matching neighborhood, we also try to explore useful information from the
connected relations. Moreover, an iterative framework is designed to leverage
the positive interactions between the entity alignment and the relation
alignment in a semi-supervised manner. Experimental results on three real-world
datasets demonstrate that the proposed model RNM performs better than
state-of-the-art methods.

本文提出了一种新的关系感知邻域匹配模型 (RNM)，通过匹配邻域和探索有用的信息来增强实体对齐，并利用半监督方法设计了一个迭代框架来利用实体对齐和关系对齐之间的正面互动，实验结果表明，RNM 模型的性能优于现有的最先进方法。