We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provably-convergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing state-of-the-art knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraint-based models doing better on graphs with a small number of highly similar relations and regularization-based models dominating for graphs with relations of varying degrees of similarity.
翻译:我们展示了将知识图表嵌入实际价值高的压强器的一套新颖方法。 这些以电压为基础的嵌入方法捕捉了以 RDF 等语义化网络语言代表的知识图中典型的定序关系。 与许多以往的模式不同, 我们的方法可以很容易地使用用户提供的先前背景知识, 或者从现有的知识图中自动提取。 除了为知识图嵌入提供更强有力的方法外, 我们还提供了一种可辨别的、 一致的、 线性强力因子化算法。 我们展示了我们模型在八种不同知识图中预测新事实的任务中的有效性, 相对于现有最新知识图嵌入技术而言, 实现了5%至50%的相对改善。 我们的经验评估表明, 当一个实体在图表中的平均程度很高时, 所有 强力分解模型都表现良好, 以约束为基础的模型在图表上表现得更好, 其关系非常相似, 以规范为基础的模型占不同程度的图表。