TempCaps: A Capsule Network-Based Embedding Model for Temporal Knowledge Graph Completion

MCML Authors

Zifeng Ding

→ Group Volker Tresp
 Database Systems, Data Mining and AI

Yunpu Ma

Dr.

→ Group Volker Tresp
 Database Systems, Data Mining and AI

Matthias Schubert

Prof. Dr.

Principal Investigator

Spatial Artificial Intelligence

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

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 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.

inproceedings

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SPNLP @ACL 2022

6th ACL Workshop on Structured Prediction for NLP at the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland, May 22-27, 2022.

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Authors

G. Fu • Z. Meng • Z. Han • Z. Ding • Y. Ma • M. Schubert • V. Tresp • R. Wattenhofer

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Links

DOI

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Research Area

 A3 | Computational Models