Accurate Coupled Tensor Factorization with Knowledge Graph

How can we accurately decompose a temporal irregular tensor along with a related knowledge graph tensor? The PARAFAC2 decomposition is widely used for analyzing irregular tensors composed of matrices with varying row sizes. Recent advancements in PARAFAC2 methods primarily focus on capturing dynamic features that change over time, since data irregularities often arise from temporal fluctuations. However, these methods often neglect static features, such as knowledge information, which remain constant over time.In this paper, we propose KG-CTF (Knowledge Graph-based Coupled Tensor Factorization), a coupled tensor factorization method designed to capture both dynamic and static features within an irregular tensor. To incorporate knowledge graph tensors as static features, KG-CTF couples an irregular temporal tensor with a knowledge graph tensor that share a common axis. Additionally, KG-CTF employs a relational regularization to capture relationships among the factor matrices of the knowledge graph tensor. For accelerated convergence of the factor matrices, KG-CTF utilizes momentum update techniques. Extensive experiments show that KG-CTF reduces error rates by up to 1.64× compared to existing PARAFAC2 methods.