Learning Sequence Encoders for Temporal Knowledge Graph Completion

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.

arXiv commentEMNLP'18

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Cited by in corpus (12)

• Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
• Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
• xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
• TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
• Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
• Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
• DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
• Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
• Temporal Knowledge Graph Reasoning Triggered by Memories
• Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs
• RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion
• Narrative Cartography with Knowledge Graphs