Learning Sequence Encoders for Temporal Knowledge Graph Completion

TL;DR

This paper introduces a method for temporal knowledge graph completion that uses recurrent neural networks to learn time-aware relation representations, improving link prediction in dynamic, real-world datasets.

Contribution

It proposes a novel approach combining RNNs with latent factorization for temporal relation encoding, addressing sparsity and heterogeneity in temporal knowledge graphs.

Findings

Improved link prediction accuracy on four temporal KGs.

Robustness to data sparsity and heterogeneity.

Effective integration of temporal information with existing methods.

Abstract

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.