  Recent studies focus on embedding learning over knowledge graphs, which map
entities and relations in knowledge graphs into low-dimensional vector spaces.
While existing models mainly consider the aspect of graph structure, there
exists a wealth of contextual and literal information that can be utilized for
more effective embedding learning. This paper introduces a novel model that
incorporates both contextual and literal information into entity and relation
embeddings by utilizing graph convolutional networks. Specifically, for
contextual information, we assess its significance through confidence and
relatedness metrics. In addition, a unique rule-based method is developed to
calculate the confidence metric, and the relatedness metric is derived from the
literal information's representations. We validate our model performance with
thorough experiments on two established benchmark datasets.
