A survey of embedding models of entities and relationships for knowledge graph completion
It is a survey paper, so it is incremental, summarizing existing work for researchers in natural language processing and knowledge graph applications.
This paper provides a comprehensive survey of embedding models for knowledge graph completion, summarizing experimental results on standard benchmarks and identifying future research directions.
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.