A survey of embedding models of entities and relationships for knowledge graph completion

Understand

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.