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

Code

• github.com/datquocnguyen/STransE
  none★ 0
• github.com/Sujit-O/pykg2vec
  tf★ 0

Abstract

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.

Tasks

Reproductions