Abstract

With the proliferation of knowledge graphs, modeling data with complex multi-relational 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, that is, 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 nonexisting 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. Supplementary files for this article are available online.

摘要

随着知识图谱的普及，具有复杂多关系结构的数据建模在统计关系学习领域越来越受到关注。统计关系学习最重要的目标之一是链接预测，即预测知识图中是否存在某些关系。已经提出了大量的模型和算法来执行链路预测，其中张量分解方法已被证明在计算效率和预测精度方面达到了最先进的性能。然而，现有张量分解模型的一个共同缺点是缺失的关系和不存在的关系以相同的方式处理，从而导致信息丢失。为了解决这个问题，我们提出了一个带有概率链接的二元张量分解模型，它不仅继承了经典张量分解模型的计算效率，而且考虑了关系数据的二元性质。我们提出的概率张量分解 (PTF) 模型在预测准确性和可解释性方面均显示出优势。本文的补充文件可在线获取。