  Predicting missing facts in a knowledge graph (KG) is a crucial task in
knowledge base construction and reasoning, and it has been the subject of much
research in recent works using KG embeddings. While existing KG embedding
approaches mainly learn and predict facts within a single KG, a more plausible
solution would benefit from the knowledge in multiple language-specific KGs,
considering that different KGs have their own strengths and limitations on data
quality and coverage. This is quite challenging, since the transfer of
knowledge among multiple independently maintained KGs is often hindered by the
insufficiency of alignment information and the inconsistency of described
facts. In this paper, we propose KEnS, a novel framework for embedding learning
and ensemble knowledge transfer across a number of language-specific KGs. KEnS
embeds all KGs in a shared embedding space, where the association of entities
is captured based on self-learning. Then, KEnS performs ensemble inference to
combine prediction results from embeddings of multiple language-specific KGs,
for which multiple ensemble techniques are investigated. Experiments on five
real-world language-specific KGs show that KEnS consistently improves
state-of-the-art methods on KG completion, via effectively identifying and
leveraging complementary knowledge.
