  Knowledge graphs (KGs) of real-world facts about entities and their
relationships are useful resources for a variety of natural language processing
tasks. However, because knowledge graphs are typically incomplete, it is useful
to perform knowledge graph completion or link prediction, i.e. predict whether
a relationship not in the knowledge graph is likely to be true. This paper
serves as a comprehensive survey of embedding models of entities and
relationships for knowledge graph completion, summarizing up-to-date
experimental results on standard benchmark datasets and pointing out potential
future research directions.
