TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
Guirong Fu | Zhao Meng | Zhen Han | Zifeng Ding | Yunpu Ma | Matthias Schubert | Volker Tresp | Roger Wattenhofer
Proceedings of the Sixth Workshop on Structured Prediction for NLP
Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.
- Zhao Meng 1
- Zhen Han 1
- Zifeng Ding 1
- Yunpu Ma 1
- Matthias Schubert 1
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