Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entities. Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones. Most of the existing work is proposed by maximizing the likelihood of observed instance-level triples. Not much attention, however, is paid to the ontological information, such as type information of entities and relations. In this work, we propose a type-augmented relation prediction (TaRP) method, where we apply both the type information and instance-level information for relation prediction. In particular, type information and instance-level information are encoded as prior probabilities and likelihoods of relations respectively, and are combined by following Bayes' rule. Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on three benchmark datasets: FB15K, YAGO26K-906, and DB111K-174. In addition, we show that TaRP achieves significantly improved data efficiency. More importantly, the type information extracted from a specific dataset can generalize well to other datasets through the proposed TaRP model.

翻译：知识图表(KGs)对于许多现实世界应用非常重要,但它们一般都因实体之间缺少关系而缺乏不完整的信息。知识图表的完成(也称为关系预测)是对现有事实进行推断的任务。大多数现有工作都是通过最大限度地增加观察到的例数三重的可能性而提出的。但是,对实体和关系等实体类型信息等肿瘤信息没有多少注意。在这项工作中,我们提出了一个类型强化关系预测(TARP)方法,我们将类型信息和实例级信息用于关系预测。特别是,类型信息和实例级信息被分别编码为先前的概率和关系可能性,并按拜斯规则加以合并。我们提议的TARP方法比三个基准数据集(FB15K、YAGO26K-906和DB111K-174)的状态方法要好得多。此外,我们还可以表明TARP取得了显著提高的数据效率。更重要的是,从一个具体数据集提取的类型信息通过一个特定数据集,通过其他数据集提取的类别信息。