# STransE: a novel embedding model of entities and relationships in knowledge bases

arXiv: https://arxiv.org/abs/1606.08140 · HTML: https://mlsanity.com/paper/1606.08140/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2016.

## Built on by (most cited first, 17 of 17)

- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Membership Inference Attacks on Knowledge Graphs](https://mlsanity.com/paper/2104.08273/) (2021)
- [Knowledge Graph Embedding: An Overview](https://mlsanity.com/paper/2309.12501/) (2023)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning](https://mlsanity.com/paper/2111.06103/) (2021)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding](https://mlsanity.com/paper/2101.10070/) (2021)
- [Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web](https://mlsanity.com/paper/2103.15950/) (2021)
- [From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding](https://mlsanity.com/paper/2303.12816/) (2023)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Principled Representation Learning for Entity Alignment](https://mlsanity.com/paper/2110.10871/) (2021)
- [A Probit Tensor Factorization Model For Relational Learning](https://mlsanity.com/paper/2111.03943/) (2021)
- [Semi-supervised Training for Knowledge Base Graph Self-attention Networks on Link Prediction](https://mlsanity.com/paper/2209.01350/) (2022)
- [ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion](https://mlsanity.com/paper/2209.02390/) (2022)
- [A Pre-training Framework for Knowledge Graph Completion](https://mlsanity.com/paper/2302.02614/) (2023)
- [Fast Knowledge Graph Completion using Graphics Processing Units](https://mlsanity.com/paper/2307.12059/) (2023)
- [On The Expressive Power of Knowledge Graph Embedding Methods](https://mlsanity.com/paper/2407.16326/) (2024)

## Abstract (arXiv)

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.