Learning Sequence Encoders for Temporal Knowledge Graph Completion
This work addresses link prediction for temporal knowledge graphs, which is an incremental improvement over static methods.
The paper tackles link prediction in temporal knowledge graphs by learning time-aware relation representations using recurrent neural networks, achieving robust performance on four datasets despite sparsity and heterogeneity of temporal expressions.
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.