A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution

TL;DR

This paper introduces a novel temporal knowledge graph completion method that emphasizes balanced timestamp distribution, significantly improving performance by directly encoding time information at the finest granularity.

Contribution

It proposes a new framework for temporal KGC that balances timestamp distribution by treating each time slice as the finest granularity, addressing limitations of previous methods.

Findings

Effective in real-world datasets

Improves temporal KGC accuracy

Balances timestamp distribution

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.