Research on link prediction in knowledge graphs has mainly focused on static
multi-relational data. In this work we consider temporal knowledge graphs where
relations between entities may only hold for a time interval or a specific
point in time. In line with previous work on static knowledge graphs, we
propose to address this problem by learning latent entity and relation type
representations. To incorporate temporal information, we utilize recurrent
neural networks to learn time-aware representations of relation types which can
be used in conjunction with existing latent factorization methods. The proposed
approach is shown to be robust to common challenges in real-world KGs: the
sparsity and heterogeneity of temporal expressions. Experiments show the
benefits of our approach on four temporal KGs. The data sets are available
under a permissive BSD-3 license 1.

本文研究了基于时间的知识图谱中的联结预测问题，提出一种使用循环神经网络和潜在因式分解相结合的方法，以学习具有时间感知的关系类型表示，以解决现实世界知识图谱中稀疏性和异质性等问题，并在四个时间知识图谱数据集上进行了实验验证。

学习序列编码器用于时间知识图谱补全

Learning Sequence Encoders for Temporal Knowledge Graph Completion

In statistical relational learning, the link prediction problem is key to
automatically understand the structure of large knowledge bases. As in previous
studies, we propose to solve this problem through latent factorization.
However, here we make use of complex valued embeddings. The composition of
complex embeddings can handle a large variety of binary relations, among them
symmetric and antisymmetric relations. Compared to state-of-the-art models such
as Neural Tensor Network and Holographic Embeddings, our approach based on
complex embeddings is arguably simpler, as it only uses the Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors. Our approach is scalable to large datasets as it remains linear in
both space and time, while consistently outperforming alternative approaches on
standard link prediction benchmarks.

该研究基于复嵌入提出了一种解决大型知识库中链接预测问题的方法，相对于其他模型而言，这种方法更简单且可扩展性好，同时在标准链接预测测试中保持了更高的性能。