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

arXiv: https://arxiv.org/abs/1703.08098 · HTML: https://mlsanity.com/paper/1703.08098/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [An Automatic Ontology Generation Framework with An Organizational Perspective](https://mlsanity.com/paper/2201.05910/) (2022)
- [Stay Positive: Knowledge Graph Embedding Without Negative Sampling](https://mlsanity.com/paper/2201.02661/) (2022)
- [MixKG: Mixing for harder negative samples in knowledge graph](https://mlsanity.com/paper/2202.09606/) (2022)
- [RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding](https://mlsanity.com/paper/2101.10070/) (2021)
- [Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models](https://mlsanity.com/paper/2203.09354/) (2022)
- [Inter-domain Multi-relational Link Prediction](https://mlsanity.com/paper/2106.06171/) (2021)
- [ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion](https://mlsanity.com/paper/2209.02390/) (2022)
- [Embedding Method for Knowledge Graph with Densely Defined Ontology](https://mlsanity.com/paper/2504.02889/) (2025)

## Abstract (arXiv)

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.