A Probit Tensor Factorization Model For Relational Learning

Abstract

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

arXiv comment30 pages

References in corpus (10)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• Dynamic stochastic blockmodels for time-evolving social networks
• TransA: An Adaptive Approach for Knowledge Graph Embedding
• Nonparametric Bayes dynamic modeling of relational data
• Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
• A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict
• Learning tensors from partial binary measurements
• Logistic Tensor Factorization for Multi-Relational Data
• Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
• Multi-Relational Learning at Scale with ADMM