# A Probit Tensor Factorization Model For Relational Learning

arXiv: https://arxiv.org/abs/2111.03943 · HTML: https://mlsanity.com/paper/2111.03943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 73 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 20 of 20)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [Latent Space Models for Dynamic Networks](https://mlsanity.com/paper/2005.08808/) (2020)
- [Multilinear tensor regression for longitudinal relational data](https://mlsanity.com/paper/1412.0048/) (2014)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts](https://mlsanity.com/paper/1506.03493/) (2015)
- [TransA: An Adaptive Approach for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05490/) (2015)
- [STransE: a novel embedding model of entities and relationships in knowledge bases](https://mlsanity.com/paper/1606.08140/) (2016)
- [Provable Sparse Tensor Decomposition](https://mlsanity.com/paper/1502.01425/) (2015)
- [Learning tensors from partial binary measurements](https://mlsanity.com/paper/1804.00108/) (2018)
- [Nonparametric Bayes dynamic modeling of relational data](https://mlsanity.com/paper/1311.4669/) (2013)
- [Equivariant and scale-free Tucker decomposition models](https://mlsanity.com/paper/1312.6397/) (2013)
- [Logistic Tensor Factorization for Multi-Relational Data](https://mlsanity.com/paper/1306.2084/) (2013)
- [Knowlege Graph Embedding by Flexible Translation](https://mlsanity.com/paper/1505.05253/) (2015)
- [Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases](https://mlsanity.com/paper/1506.00999/) (2015)
- [Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss](https://mlsanity.com/paper/1303.1733/) (2013)
- [Multi-Relational Learning at Scale with ADMM](https://mlsanity.com/paper/1604.00647/) (2016)

## Abstract (arXiv)

With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational learning is link prediction, i.e., predicting whether certain relations exist in the knowledge graph. A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy. However, a common drawback of the existing tensor factorization models is that the missing relations and non-existing relations are treated in the same way, which results in a loss of information. To address this issue, we propose a binary tensor factorization model with probit link, which not only inherits the computation efficiency from the classic tensor factorization model but also accounts for the binary nature of relational data. Our proposed probit tensor factorization (PTF) model shows advantages in both the prediction accuracy and interpretability