  Entities may have complex interactions in a knowledge graph (KG), such as
multi-step relationships, which can be viewed as graph contextual information
of the entities. Traditional knowledge representation learning (KRL) methods
usually treat a single triple as a training unit, and neglect most of the graph
contextual information exists in the topological structure of KGs. In this
study, we propose a Path-based Pre-training model to learn Knowledge
Embeddings, called PPKE, which aims to integrate more graph contextual
information between entities into the KRL model. Experiments demonstrate that
our model achieves state-of-the-art results on several benchmark datasets for
link prediction and relation prediction tasks, indicating that our model
provides a feasible way to take advantage of graph contextual information in
KGs.
