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