# A Relational Tucker Decomposition for Multi-Relational Link Prediction

arXiv: https://arxiv.org/abs/1902.00898 · HTML: https://mlsanity.com/paper/1902.00898/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [A Contextualized BERT model for Knowledge Graph Completion](https://mlsanity.com/paper/2412.11016/) (2024)
- [MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion](https://mlsanity.com/paper/2503.03091/) (2025)
- [KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models](https://mlsanity.com/paper/2508.15357/) (2025)
- [When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking](https://mlsanity.com/paper/2606.10287/) (2026)

## Abstract (arXiv)

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use the RT decomposition as a tool to explore whether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperform sparse models (using the same number of parameters). Our experiments indicate that---depending on the dataset--both questions can be answered affirmatively.