基于知识丰富张量分解的知识图谱事实预测
Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization
摘要：
我们提出了一个家庭的新方法嵌入知识图谱到实值张量。这些基于张量的嵌入捕获了由语义Web语言（如RDF）表示的知识图谱中典型的有序关系。与许多以前的模型不同，我们的方法可以很容易地使用用户提供的或从现有的知识图谱自动提取的先验背景知识。除了为知识图谱嵌入提供更鲁棒的方法外，我们还提供了一个可证明收敛的线性张量分解算法。我们证明了我们的模型在预测八个不同知识图谱的新事实的任务中的有效性，与现有的最先进的知识图谱嵌入技术相比，实现了5%到50%的相对改进。我们的实证评估表明，当图中实体的平均度很高时，所有的张量分解模型都表现良好，基于约束的模型在具有少量高度相似关系的图上表现更好，而基于正则化的模型在具有不同相似度关系的图中占主导地位。
英文摘要：
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.
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