  Knowledge graphs suffer from sparsity which degrades the quality of
representations generated by various methods. While there is an abundance of
textual information throughout the web and many existing knowledge bases,
aligning information across these diverse data sources remains a challenge in
the literature. Previous work has partially addressed this issue by enriching
knowledge graph entities based on "hard" co-occurrence of words present in the
entities of the knowledge graphs and external text, while we achieve "soft"
augmentation by proposing a knowledge graph enrichment and embedding framework
named Edge. Given an original knowledge graph, we first generate a rich but
noisy augmented graph using external texts in semantic and structural level. To
distill the relevant knowledge and suppress the introduced noise, we design a
graph alignment term in a shared embedding space between the original graph and
augmented graph. To enhance the embedding learning on the augmented graph, we
further regularize the locality relationship of target entity based on negative
sampling. Experimental results on four benchmark datasets demonstrate the
robustness and effectiveness of Edge in link prediction and node
classification.
