  Background Knowledge graphs (KGs), especially medical knowledge graphs, are
often significantly incomplete, so it necessitating a demand for medical
knowledge graph completion (MedKGC). MedKGC can find new facts based on the
exited knowledge in the KGs. The path-based knowledge reasoning algorithm is
one of the most important approaches to this task. This type of method has
received great attention in recent years because of its high performance and
interpretability. In fact, traditional methods such as path ranking algorithm
(PRA) take the paths between an entity pair as atomic features. However, the
medical KGs are very sparse, which makes it difficult to model effective
semantic representation for extremely sparse path features. The sparsity in the
medical KGs is mainly reflected in the long-tailed distribution of entities and
paths. Previous methods merely consider the context structure in the paths of
the knowledge graph and ignore the textual semantics of the symbols in the
path. Therefore, their performance cannot be further improved due to the two
aspects of entity sparseness and path sparseness. To address the above issues,
this paper proposes two novel path-based reasoning methods to solve the
sparsity issues of entity and path respectively, which adopts the textual
semantic information of entities and paths for MedKGC. By using the pre-trained
model BERT, combining the textual semantic representations of the entities and
the relationships, we model the task of symbolic reasoning in the medical KG as
a numerical computing issue in textual semantic representation.
