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
Abstract
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 networkbased 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 1 .