We consider two popular approaches to Knowledge Graph Completion (KGC):
textual models that rely on textual entity descriptions, and structure-based
models that exploit the connectivity structure of the Knowledge Graph (KG).
Preliminary experiments show that these approaches have complementary
strengths: structure-based models perform well when the gold answer is easily
reachable from the query head in the KG, while textual models exploit
descriptions to give good performance even when the gold answer is not
reachable. In response, we explore ensembling as a way of combining the best of
both approaches. We propose a novel method for learning query-dependent
ensemble weights by using the distributions of scores assigned by individual
models to all candidate entities. Our ensemble baseline achieves
state-of-the-art results on three standard KGC datasets, with up to 6.8 pt MRR
and 8.3 pt Hits@1 gains over best individual models.

我们研究了两种知识图谱补全方法：依赖文本实体描述的文本模型和利用知识图谱的连接结构的基于结构的模型。我们通过集成的方式结合了这两种方法，提出了一种新颖的方法来学习查询相关的集成权重，并在三个标准知识图谱补全数据集上取得了最先进的结果。