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.

本文提出了一族新的方法，将知识图谱嵌入到实值张量中，使用这些基于张量的嵌入可以更加准确地预测新事实。通过实证评估，证明了这些张量分解模型的有效性和可靠性。