Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of
inferring missing information from knowledge graphs, is a widely used task in
many applications, such as product recommendation and question answering. The
state-of-the-art approaches of knowledge graph embeddings and/or rule mining
and reasoning are data-driven and, thus, solely based on the information the
input knowledge graph contains. This leads to unsatisfactory prediction results
which make such solutions inapplicable to crucial domains such as healthcare.
To further enhance the accuracy of knowledge graph completion we propose to
loosely-couple the data-driven power of knowledge graph embeddings with
domain-specific reasoning stemming from experts or entailment regimes (e.g.,
OWL2). In this way, we not only enhance the prediction accuracy with domain
knowledge that may not be included in the input knowledge graph but also allow
users to plugin their own knowledge graph embedding and reasoning method. Our
initial results show that we enhance the MRR accuracy of vanilla knowledge
graph embeddings by up to 3x and outperform hybrid solutions that combine
knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.

通过将知识图谱嵌入的数据驱动能力与领域专家或包含 OWL2 的蕴涵方案等领域特定推理松散地耦合，我们不仅能够提高预测精度，而且还允许用户插入自己的知识图谱嵌入和推理方法。我们的初步结果表明，我们的方法将基准知识图嵌入的平均倒数排名（MRR）精度提高了最高 3 倍，并且超过了将知识图嵌入与规则挖掘和推理相结合的混合解决方案高达 3.5 倍 MRR。