Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method.
@article{arxiv.2108.13024,
title = {A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution},
author = {Kangzheng Liu and Yuhong Zhang},
journal= {arXiv preprint arXiv:2108.13024},
year = {2021}
}