# Knowlege Graph Embedding by Flexible Translation

arXiv: https://arxiv.org/abs/1505.05253 · HTML: https://mlsanity.com/paper/1505.05253/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2015.

## Built on by (most cited first, 3 of 3)

- [M"obiusE: Knowledge Graph Embedding on M"obius Ring](https://mlsanity.com/paper/2101.02352/) (2021)
- [Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering](https://mlsanity.com/paper/2302.11880/) (2023)
- [A Probit Tensor Factorization Model For Relational Learning](https://mlsanity.com/paper/2111.03943/) (2021)

## Abstract (arXiv)

Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. State-of-the-art methods, such as TransE, TransH, and TransR build embeddings by treating relation as translation from head entity to tail entity. However, previous models can not deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or lack of scalability and efficiency. Thus, we propose a novel method, flexible translation, named TransF, to address the above issues. TransF regards relation as translation between head entity vector and tail entity vector with flexible magnitude. To evaluate the proposed model, we conduct link prediction and triple classification on benchmark datasets. Experimental results show that our method remarkably improve the performance compared with several state-of-the-art baselines.