  Knowledge graphs, as the cornerstone of many AI applications, usually face
serious incompleteness problems. In recent years, there have been many efforts
to study automatic knowledge graph completion (KGC), most of which use existing
knowledge to infer new knowledge. However, in our experiments, we find that not
all relations can be obtained by inference, which constrains the performance of
existing models. To alleviate this problem, we propose a new model based on
information retrieval and reading comprehension, namely IR4KGC. Specifically,
we pre-train a knowledge-based information retrieval module that can retrieve
documents related to the triples to be completed. Then, the retrieved documents
are handed over to the reading comprehension module to generate the predicted
answers. In experiments, we find that our model can well solve relations that
cannot be inferred from existing knowledge, and achieve good results on KGC
datasets.
