  Knowledge Graph Completion is a task of expanding the knowledge graph/base
through estimating possible entities, or proper nouns, that can be connected
using a set of predefined relations, or verb/predicates describing
interconnections of two things. Generally, we describe this problem as adding
new edges to a current network of vertices and edges. Traditional approaches
mainly focus on using the existing graphical information that is intrinsic of
the graph and train the corresponding embeddings to describe the information;
however, we think that the corpus that are related to the entities should also
contain information that can positively influence the embeddings to better make
predictions. In our project, we try numerous ways of using extracted or raw
textual information to help existing KG embedding frameworks reach better
prediction results, in the means of adding a similarity function to the
regularization part in the loss function. Results have shown that we have made
decent improvements over baseline KG embedding methods.
