Open Access

ARTICLE

Learning Time Embedding for Temporal Knowledge Graph Completion

Jinglu Chen¹, Mengpan Chen², Wenhao Zhang^2,*, Huihui Ren², Daniel Dajun Zeng^1,2

1 College of Management and Economics, Tianjin University, Tianjin, 300072, China
2 The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

* Corresponding Author: Wenhao Zhang. Email:

Computers, Materials & Continua 2026, 86(2), 1-25. https://doi.org/10.32604/cmc.2025.069331

Received 20 June 2025; Accepted 18 September 2025; Issue published 09 December 2025

Abstract

Temporal knowledge graph completion (TKGC), which merges temporal information into traditional static knowledge graph completion (SKGC), has garnered increasing attention recently. Among numerous emerging approaches, translation-based embedding models constitute a prominent approach in TKGC research. However, existing translation-based methods typically incorporate timestamps into entities or relations, rather than utilizing them independently. This practice fails to fully exploit the rich semantics inherent in temporal information, thereby weakening the expressive capability of models. To address this limitation, we propose embedding timestamps, like entities and relations, in one or more dedicated semantic spaces. After projecting all embeddings into a shared space, we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities. Our method elevates timestamps to the same representational significance as entities and relations. Based on this strategy, we introduce two novel translation-based embedding models: TE-TransR and TE-TransT. With the independent representation of timestamps, our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task, namely time prediction. To further bolster the precision and reliability of time prediction, we introduce a granular, time unit-based timestamp setting and a relation-specific evaluation protocol. Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks, with TE-TransR outperforming existing baselines in the time prediction task.

---

Keywords

---