  Knowledge graphs have evolved rapidly in recent years and their usefulness
has been demonstrated in many artificial intelligence tasks. However, knowledge
graphs often have lots of missing facts. To solve this problem, many knowledge
graph embedding models have been developed to populate knowledge graphs and
these have shown outstanding performance. However, knowledge graph embedding
models are so-called black boxes, and the user does not know how the
information in a knowledge graph is processed and the models can be difficult
to interpret. In this paper, we utilize graph patterns in a knowledge graph to
overcome such problems. Our proposed model, the {\it graph pattern entity
ranking model} (GRank), constructs an entity ranking system for each graph
pattern and evaluates them using a ranking measure. By doing so, we can find
graph patterns which are useful for predicting facts. Then, we perform link
prediction tasks on standard datasets to evaluate our GRank method. We show
that our approach outperforms other state-of-the-art approaches such as ComplEx
and TorusE for standard metrics such as HITS@{\it n} and MRR. Moreover, our
model is easily interpretable because the output facts are described by graph
patterns.
