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

## TL;DR

This survey reviews embedding models for knowledge graph completion, summarizing recent experimental results and highlighting future research directions to improve link prediction accuracy.

## Contribution

It provides a comprehensive overview of current embedding models for entities and relationships in knowledge graphs, including experimental comparisons and future research insights.

## Key findings

- Embedding models vary in effectiveness across datasets
- Recent models achieve higher accuracy in link prediction
- Future research should focus on model scalability and interpretability

## 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.