A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution

Kangzheng Liu; Yuhong Zhang

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arXiv · 2108.13024

A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution

Kangzheng Liu·Yuhong Zhang

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Abstract

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.

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BibTeX RIS

Kangzheng Liu, Yuhong Zhang. 2021-11-06. A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution. https://arxiv.org/abs/2108.13024

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