# Logistic Tensor Factorization for Multi-Relational Data

arXiv: https://arxiv.org/abs/1306.2084 · HTML: https://mlsanity.com/paper/1306.2084/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2013.

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

- [Graph Embeddings via Tensor Products and Approximately Orthonormal Codes](https://mlsanity.com/paper/2208.10917/) (2022)
- [A generalizable framework for low-rank tensor completion with numerical priors](https://mlsanity.com/paper/2302.05881/) (2023)
- [A Probit Tensor Factorization Model For Relational Learning](https://mlsanity.com/paper/2111.03943/) (2021)

## Abstract (arXiv)

Tensor factorizations have become increasingly popular approaches for various learning tasks on structured data. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for the binary nature of adjacency tensors. We study the improvements that can be gained via this approach on various benchmark datasets and show that the logistic extension can improve the prediction results significantly.